diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..8af8ef7d61d91d42ac078eb6849bd363d61ecfd5 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+core/bicep_model/train.csv filter=lfs diff=lfs merge=lfs -text
+core/lunge_model/err.train.csv filter=lfs diff=lfs merge=lfs -text
+core/lunge_model/stage.train.csv filter=lfs diff=lfs merge=lfs -text
+core/plank_model/train.csv filter=lfs diff=lfs merge=lfs -text
+demo/bc_demo.mp4 filter=lfs diff=lfs merge=lfs -text
+demo/lunge_demo.mp4 filter=lfs diff=lfs merge=lfs -text
+demo/plank_demo.mp4 filter=lfs diff=lfs merge=lfs -text
+demo/squat_demo.mp4 filter=lfs diff=lfs merge=lfs -text
+docs/B1809677-Ngô[[:space:]]Hồng[[:space:]]Quốc[[:space:]]Bảo-Wrong[[:space:]]Pose[[:space:]]Dectection[[:space:]]based[[:space:]]on[[:space:]]Machine[[:space:]]Learning.docx filter=lfs diff=lfs merge=lfs -text
+docs/B1809677-Ngô[[:space:]]Hồng[[:space:]]Quốc[[:space:]]Bảo-Wrong[[:space:]]Pose[[:space:]]Dectection[[:space:]]based[[:space:]]on[[:space:]]Machine[[:space:]]Learning.pdf filter=lfs diff=lfs merge=lfs -text
+images/bicep_curl.gif filter=lfs diff=lfs merge=lfs -text
+images/lunge.gif filter=lfs diff=lfs merge=lfs -text
+images/plank.gif filter=lfs diff=lfs merge=lfs -text
+images/squat.gif filter=lfs diff=lfs merge=lfs -text
+images/web_3.png filter=lfs diff=lfs merge=lfs -text
+images/web_4.png filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b6c5e651a09db640e08dbcd2cc96930ea8b49775
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,52 @@
+# Files
+.DS_Store
+
+# Machine Learning Folders
+.ipynb_checkpoints/
+env/
+data/
+
+# Editor directories and files
+.vscode
+!.vscode/extensions.json
+.idea
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+# Vue clients
+package-lock.json
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+coverage
+*.local
+
+/cypress/videos/
+/cypress/screenshots/
+
+# Keras Tuner
+**/keras_tuner_dir
+
+# Django server
+__pycache__/
+db.sqlite3
+temp/
+
+# References
+github/
+web/*/static/media/
+web/*/static/images/
+web/*/static/assets/
+web/*/static/css/
+*.env
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..f303e86e4d1fd108ead04ee3a2d2433bf37296e1
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,36 @@
+FROM python:3.8
+
+# Install Node.js and npm
+RUN apt-get update && apt-get install -y curl && \
+    curl -sL https://deb.nodesource.com/setup_16.x | bash - && \
+    apt-get install -y nodejs \
+    npm
+
+RUN apt-get update && apt-get install -y rsync
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+
+RUN export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# copy /web folder and install client's dependencies
+COPY ./web /app
+WORKDIR /app
+RUN npm run install:client
+RUN npm run build-deploy:client
+
+# Expose port 8000 for the Django server
+EXPOSE 8000
+
+# Start the server
+CMD ["python", "server/manage.py", "runserver", "0.0.0.0:8000"]
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..8a8181dc7c63e8b6995d6338278698f030a645dc
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Ngô Hồng Quốc Bảo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 7555082ef27f02524bc90eda92f7b13390c57d37..624ef27724bb7906c1e872c1b31dededdd1c132d 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,168 @@
----
-title: Yoga
-emoji: 📉
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<div id="top"></div>
+
+<!-- PROJECT LOGO -->
+<br />
+<div align="center">
+    <a href="https://github.com/NgoQuocBao1010/Exercise-Correction">
+        <img src="./images/logo.png" alt="Logo" width="60%">
+    </a>
+
+  <h2 align="center">Exercise Pose Correction</h2>
+
+  <p align="center">
+    Make use of the power of Mediapipe’s pose detection, this project is built in order to analyze, detect and classifying the forms of fitness exercises.
+  </p>
+</div>
+
+<!-- ABOUT THE PROJECT -->
+
+## About The Project
+
+This project goal is to develop 4 machine learning models for 4 of the most home exercises **(Bicep Curl, Plank, Squat and Lunge)** which each model can detect any form of incorrect movement while a person is performing a correspond exercise. In addition, a web application that utilize the trained models, will be built in other to analyze and provide feedbacks on workout videos.
+
+Here are some detections of the exercises:
+
+-   Bicep Curl
+<p align="center"><img src="images/bicep_curl.gif" alt="Logo" width="70%"></p>
+
+-   Basic Plank
+<p align="center"><img src="images/plank.gif" alt="Logo" width="70%"></p>
+
+-   Basic Squat
+<p align="center"><img src="images/squat.gif" alt="Logo" width="70%"></p>
+
+-   Lunge
+<p align="center"><img src="images/lunge.gif" alt="Logo" width="70%"></p>
+
+-   Models' evaluation results and website screenshots [here](#usage)
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+### Built With
+
+1. For data processing and model training
+
+    - [Numpy](https://numpy.org/)
+    - [Pandas](https://pandas.pydata.org/)
+    - [Sklearn](https://scikit-learn.org/stable/)
+    - [Keras](https://keras.io/)
+
+1. For building website
+
+    - [Vue.js v3](https://vuejs.org/)
+    - [Django](https://www.djangoproject.com/)
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+## Dataset
+
+Due to the lack of videos or dataset online that recorded human doing exercises both in a proper or improper way, the majority of self-collected videos were either recorded by myself, my friends or my family. The majority of the collected videos were removed due to privacy purpose.
+
+With an exercise such as Plank, as there is not much movement during the exercise, I’m able to find a dataset from an open database from [Kaggle](https://www.kaggle.com/datasets/niharika41298/yoga-poses-dataset). The found dataset is about many yoga poses but the very well-known ones are the downward dog pose, goddess pose, tree pose, plank pose and the warrior pose. The dataset contains 5 folders for 5 poses, each folder contains images of people correctly doing the correspond pose.
+
+For the purpose of this thesis, only the folder contains the images of people properly doing plank is chosen. There are 266 image files in that folder, I handpicked all the images that represent a basic plank and discard the reset. In conclusion, there are 30 images which are arranged to the proper form class for basic plank.
+
+## Getting Started
+
+This is an example of how you may give instructions on setting up the project locally.
+
+#### Setting Up Environment
+
+```
+    Python 3.8.13
+    Node 17.8.0
+    NPM 8.5.5
+    OS: Linux or MacOS
+```
+
+```markdown
+    NOTES
+    ⚠️ Commands/Scripts for this project are wrote for Linux-based OS. They may not work on Windows machines.
+```
+
+### Installation
+
+_If you only want to try the website, look [here](./web/README.md)._
+
+1. Clone the repo and change directory to that folder
+
+    ```sh
+    git clone https://github.com/NgoQuocBao1010/Exercise-Correction.git
+    ```
+
+1. Install all project dependencies
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+1. Folder **_[core](./core/README.md)_** is the code for data processing and model training.
+1. Folder **_[web](./web/README.md)_** is the code for website.
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+<!-- USAGE EXAMPLES -->
+<div id="Usage"></div>
+<br/>
+
+## Usage
+
+As the introduction indicated, there are 2 purposes for this project.
+
+1. Model training **(describe in depth [here](core/README.md))**. Below are the evaluation results for each models.
+
+    - [Bicep Curl](core/bicep_model/README.md) - _lean back error_: Confusion Matrix - ROC curve
+      | <img align="center" alt="Bicep Curl evaluation" src="images/bicep_curl_eval.png" /> | <img align="center" alt="NgoQuocBao's Top Languages" src="images/bicep_curl_eval_2.png" /> |
+      | ------------- | ------------- |
+    - [Plank](core/plank_model/README.md) - _all errors_: Confusion Matrix - ROC curve
+      | <img align="center" alt="Plank evaluation" src="images/plank_eval.png" /> | <img align="center" alt="NgoQuocBao's Top Languages" src="images/plank_eval_2.png" /> |
+      | ------------- | ------------- |
+    - [Basic Squat](core/squat_model/README.md) - _stage_: Confusion Matrix - ROC curve
+      | <img align="center" alt="Squat evaluation" src="images/squat_eval.png" /> | <img align="center" alt="NgoQuocBao's Top Languages" src="images/squat_eval_2.png" /> |
+      | ------------- | ------------- |
+    - [Lunge](core/lunge_model/README.md) - _knee over toe error_: Confusion Matrix - ROC curve
+      | <img align="center" alt="Lunge evaluation" src="images/lunge_eval.png" /> | <img align="center" alt="NgoQuocBao's Top Languages" src="images/lunge_eval_2.png" /> |
+      | ------------- | ------------- |
+
+1. Website for exercise detection. This web is for demonstration purpose of all the trained models, therefore, at the moment there are only 1 main features: Analyzing and giving feedbacks on user's exercise video.
+ <p align="center"><img src="images/web_1.png" alt="Logo" width="70%"></p>
+ <p align="center"><img src="images/web_2.png" alt="Logo" width="70%"></p>
+ <p align="center"><img src="images/web_3.png" alt="Logo" width="70%"></p>
+ <p align="center"><img src="images/web_4.png" alt="Logo" width="70%"></p>
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+<!-- CONTRIBUTING -->
+
+## Contributing
+
+Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
+
+If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
+Don't forget to give the project a star! Thanks again!
+
+1. Fork the Project
+2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the Branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+<!-- LICENSE -->
+
+## License
+
+Distributed under the MIT License.
+
+<p align="right">(<a href="#top">back to top</a>)</p>
+
+<!-- ACKNOWLEDGMENTS -->
+
+## Acknowledgments
+
+-   Here are some other projects which I get inspired from: [Pose Trainer](https://github.com/stevenzchen/pose-trainer), [Deep Learning Fitness Exercise Correction Keras](https://github.com/Vollkorn01/Deep-Learning-Fitness-Exercise-Correction-Keras) and [Posture](https://github.com/twixupmysleeve/Posture).
+-   [Logo marker](https://www4.flamingtext.com/) for this project.
+-   This awesome README template is from [Best README Template](https://github.com/othneildrew/Best-README-Template). ♥
+
+<p align="right">(<a href="#top">back to top</a>)</p>
diff --git a/core/README.md b/core/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..093a89642b6b2a4f869f3896c461e2a6a7d95b64
--- /dev/null
+++ b/core/README.md
@@ -0,0 +1,58 @@
+<h2 align="center">Build Machine Learning Model</h2>
+
+Brief overview about the methodology of building models for exercise pose detection.
+To go in depth on each exercise, click the link below:
+
+-   [Bicep Curl](./bicep_model/README.md)
+-   [Plank](./plank_model/README.md)
+-   [Basic Squat](./squat_model/README.md)
+-   [Lunge](./lunge_model/README.md)
+
+### 1. Simple error detection
+
+For some simple errors (for example, the feet placement error in squat), the detection method is either measuring the distance/angle between different joints during the exercise with the coordinate outputs from MediaPipe Pose.
+
+-   **_Distance Calculation_**
+    Assume there are 2 points with the following coordinates: Point 1 (x1,y1) and Point 2 (x2,y2), below is the formula to calculate the distance between 2 points.
+
+    ```
+    distance= √((x1-x2)^2 +(y1-y2) ^2 )
+    ```
+
+-   **_Angle Calculation_**
+    Assume there are 3 points with the following coordinates: Point 1 (x1,y1), Point 2 (x2,y2) and Point 3 (x3,y3), below is the formula to calculate the angle created by 3 points.
+    ```
+    angle_in_radian =arctan2⁡(y3-y2,x3-x2) -arctan2(y1-y2,x1-x2)
+    angle_in_degree=(angle_in_rad \* 180)/Π
+    ```
+
+### 2. Model Training for Error Detection
+
+#### 1. Pick important landmarks
+
+For each exercise, there will be different poses/body’s position, therefore it is essential to identify which parts (shoulder, hip, …) of a body are contribute to the exercise. The important landmarks identified for each exercise are utilized to extract body part’s position while exercising using MediaPipe.
+
+#### 2. Data Processing
+
+ <p align="center"><img src="../images/data_processing.png" alt="Logo" width="70%" style="background-color:#f5f5f5"></p>
+
+#### 3. Model training
+
+There are 2 methods used in this thesis for model training. For each exercise, the models trained for each method will be compared and the best model will be chosen.
+
+-   Classification with Scikit-learn. (Decision Tree/Random Forest (RF), K-Nearest Neighbors (KNN), C-Support Vector (SVC), Logistic Regression classifier (LR) and Stochastic Gradient Descent classifier (SGDC)).
+-   Building a Neural Network for classification with Keras.
+
+### 3. Evaluation results of all models
+
+1. Bicep Curl - _lean back error_
+ <p align="center"><img src="../images/bicep_curl_eval_3.png" alt="Logo" width="70%"></p>
+
+2. Plank - _all errors_
+ <p align="center"><img src="../images/plank_eval_3.png" alt="Logo" width="70%"></p>
+
+3. Basic Squat - _stage_
+ <p align="center"><img src="../images/squat_eval_3.png" alt="Logo" width="70%"></p>
+
+4. Lunge - _knee over toe error_
+ <p align="center"><img src="../images/lunge_eval_3.png" alt="Logo" width="70%"></p>
diff --git a/core/bicep_model/1.data.ipynb b/core/bicep_model/1.data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..29fcdfb3c16cc3dd735aeb4260b15be88c9d6cf5
--- /dev/null
+++ b/core/bicep_model/1.data.ipynb
@@ -0,0 +1,510 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[67754]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10a8c8860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x161476480). One of the two will be used. Which one is undefined.\n",
+      "objc[67754]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x10567ca68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x1614764d0). One of the two will be used. Which one is undefined.\n",
+      "objc[67754]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x10567ca90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x1614764f8). One of the two will be used. Which one is undefined.\n",
+      "objc[67754]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x10567cab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x161476520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os, csv\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Describe the data gathering process and build dataset from Video\n",
+    "\n",
+    "The purpose is to gather data to determine the correct standing posture for Bicep Curl exercise\n",
+    "There are 2 stages:\n",
+    "- Correct: \"C\"\n",
+    "- Lean-back-error: \"L\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2. Set up important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "    \n",
+    "\n",
+    "def init_csv(dataset_path: str):\n",
+    "    '''\n",
+    "    Create a blank csv file with just columns\n",
+    "    '''\n",
+    "\n",
+    "    # Ignore if file is already exist\n",
+    "    if os.path.exists(dataset_path):\n",
+    "        return\n",
+    "\n",
+    "    # Write all the columns to a empty file\n",
+    "    with open(dataset_path, mode=\"w\", newline=\"\") as f:\n",
+    "        csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "        csv_writer.writerow(HEADERS)\n",
+    "\n",
+    "\n",
+    "def export_landmark_to_csv(dataset_path: str, results, action: str) -> None:\n",
+    "    '''\n",
+    "    Export Labeled Data from detected landmark to csv\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "    keypoints = []\n",
+    "\n",
+    "    try:\n",
+    "        # Extract coordinate of important landmarks\n",
+    "        for lm in IMPORTANT_LMS:\n",
+    "            keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "            keypoints.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "        \n",
+    "        keypoints = list(np.array(keypoints).flatten())\n",
+    "\n",
+    "        # Insert action as the label (first column)\n",
+    "        keypoints.insert(0, action)\n",
+    "\n",
+    "        # Append new row to .csv file\n",
+    "        with open(dataset_path, mode=\"a\", newline=\"\") as f:\n",
+    "            csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "            csv_writer.writerow(keypoints)\n",
+    "        \n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_train.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "    \n",
+    "\n",
+    "def concat_csv_files_with_same_headers(file_paths: list, saved_path: str):\n",
+    "    '''\n",
+    "    Concat different csv files\n",
+    "    '''\n",
+    "    all_df = []\n",
+    "    for path in file_paths:\n",
+    "        df = pd.read_csv(path, index_col=None, header=0)\n",
+    "        all_df.append(df)\n",
+    "    \n",
+    "    results = pd.concat(all_df, axis=0, ignore_index=True)\n",
+    "    results.to_csv(saved_path, sep=',', encoding='utf-8', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Extract data from video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "DATASET_PATH = \"train.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/db_curl/stand_posture_11.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "# init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"Cannot detect pose - No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # Press C to save as correct form\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as low back\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# csv_files = [os.path.join(\"./\", f) for f in os.listdir(\"./\") if \"csv\" in f]\n",
+    "\n",
+    "# concat_csv_files_with_same_headers(csv_files, \"train.csv\")\n",
+    "\n",
+    "df = describe_dataset(\"./train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Clean Data and Visualize data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remove_duplicate_rows(\"./train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 15372 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    8238\n",
+      "L    7134\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='label', ylabel='count'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(\"./train.csv\")\n",
+    "sns.countplot(x='label', data=df, palette=\"Set1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Gather Test Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TEST_DATASET_PATH = \"test.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/db_curl/bc_test_2.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "init_csv(TEST_DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"Cannot detect pose - No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(10) & 0xFF\n",
+    "\n",
+    "        # Press C to save as correct form\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as low back\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 604 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    339\n",
+      "L    265\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='count', ylabel='label'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(TEST_DATASET_PATH)\n",
+    "sns.countplot(y='label', data=test_df, palette=\"Set1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/bicep_model/2.sklearn.ipynb b/core/bicep_model/2.sklearn.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0c6fa0382ca44930fdfba38902968ce8ff3de152
--- /dev/null
+++ b/core/bicep_model/2.sklearn.ipynb
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[49355]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x108688860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece480). One of the two will be used. Which one is undefined.\n",
+      "objc[49355]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[49355]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[49355]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103440ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x160ece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Train model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.1. Describe data and split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 15372 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    8238\n",
+      "L    7134\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "df = describe_dataset(\"./train.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "x = df.drop(\"label\", axis = 1)\n",
+    "x = pd.DataFrame(sc.transform(x))\n",
+    "\n",
+    "y = df[\"label\"].astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9465     1\n",
+       "8833     0\n",
+       "6190     0\n",
+       "7645     0\n",
+       "13890    1\n",
+       "        ..\n",
+       "11468    1\n",
+       "7221     1\n",
+       "1318     1\n",
+       "8915     1\n",
+       "11055    1\n",
+       "Name: label, Length: 12297, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1234)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2. Train model using Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>0.999024</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>[[1677, 2], [1, 1395]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[0.997, 0.999]</td>\n",
+       "      <td>0.998049</td>\n",
+       "      <td>[0.999, 0.996]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[1678, 1], [5, 1391]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[0.997, 0.995]</td>\n",
+       "      <td>0.996098</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[[1672, 7], [5, 1391]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.997, 0.991]</td>\n",
+       "      <td>0.994146</td>\n",
+       "      <td>[0.992, 0.996]</td>\n",
+       "      <td>[0.995, 0.994]</td>\n",
+       "      <td>[[1666, 13], [5, 1391]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.987, 0.974]</td>\n",
+       "      <td>0.981463</td>\n",
+       "      <td>[0.979, 0.985]</td>\n",
+       "      <td>[0.983, 0.98]</td>\n",
+       "      <td>[[1643, 36], [21, 1375]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.986, 0.975]</td>\n",
+       "      <td>0.980813</td>\n",
+       "      <td>[0.979, 0.983]</td>\n",
+       "      <td>[0.982, 0.979]</td>\n",
+       "      <td>[[1644, 35], [24, 1372]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.927, 0.842]</td>\n",
+       "      <td>0.884878</td>\n",
+       "      <td>[0.857, 0.918]</td>\n",
+       "      <td>[0.89, 0.879]</td>\n",
+       "      <td>[[1439, 240], [114, 1282]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model Precision Score  Accuracy score    Recall Score        F1 score  \\\n",
+       "0    RF  [0.999, 0.999]        0.999024  [0.999, 0.999]  [0.999, 0.999]   \n",
+       "1   KNN  [0.997, 0.999]        0.998049  [0.999, 0.996]  [0.998, 0.998]   \n",
+       "2   SVC  [0.997, 0.995]        0.996098  [0.996, 0.996]  [0.996, 0.996]   \n",
+       "3   DTC  [0.997, 0.991]        0.994146  [0.992, 0.996]  [0.995, 0.994]   \n",
+       "4  SGDC  [0.987, 0.974]        0.981463  [0.979, 0.985]   [0.983, 0.98]   \n",
+       "5    LR  [0.986, 0.975]        0.980813  [0.979, 0.983]  [0.982, 0.979]   \n",
+       "6    NB  [0.927, 0.842]        0.884878  [0.857, 0.918]   [0.89, 0.879]   \n",
+       "\n",
+       "             Confusion Matrix  \n",
+       "0      [[1677, 2], [1, 1395]]  \n",
+       "1      [[1678, 1], [5, 1391]]  \n",
+       "2      [[1672, 7], [5, 1391]]  \n",
+       "3     [[1666, 13], [5, 1391]]  \n",
+       "4    [[1643, 36], [21, 1375]]  \n",
+       "5    [[1644, 35], [24, 1372]]  \n",
+       "6  [[1439, 240], [114, 1282]]  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algorithms =[(\"LR\", LogisticRegression()),\n",
+    "         (\"SVC\", SVC(probability=True)),\n",
+    "         ('KNN',KNeighborsClassifier()),\n",
+    "         (\"DTC\", DecisionTreeClassifier()),\n",
+    "         (\"SGDC\", CalibratedClassifierCV(SGDClassifier())),\n",
+    "         (\"NB\", GaussianNB()),\n",
+    "         ('RF', RandomForestClassifier()),]\n",
+    "\n",
+    "models = {}\n",
+    "final_results = []\n",
+    "\n",
+    "for name, model in algorithms:\n",
+    "    trained_model = model.fit(X_train, y_train)\n",
+    "    models[name] = trained_model\n",
+    "\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(X_test)\n",
+    "\n",
+    "    p_score = precision_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    a_score = accuracy_score(y_test, model_results)\n",
+    "    r_score = recall_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    f1_score_result = f1_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    cm = confusion_matrix(y_test, model_results, labels=[0, 1])\n",
+    "    final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm))\n",
+    "\n",
+    "# Sort results by F1 score\n",
+    "final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3. Dump models pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/all_sklearn.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(sc, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/bicep_model/3.deep_learning.ipynb b/core/bicep_model/3.deep_learning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..127ffe8e22810036fed23f03f2fb631f48d76b29
--- /dev/null
+++ b/core/bicep_model/3.deep_learning.ipynb
@@ -0,0 +1,1312 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data visualization\n",
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "\n",
+    "# Keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "from keras.callbacks import EarlyStopping, TensorBoard\n",
+    "import keras_tuner as kt\n",
+    "\n",
+    "# Train-Test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# Classification Report\n",
+    "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Important Landmarks and Important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "# Remove duplicate rows (optional)\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_train.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Describe Dataset & Split Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 15372 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    8238\n",
+      "L    7134\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "df = describe_dataset(\"./train.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "x = df.drop(\"label\", axis = 1)\n",
+    "x = pd.DataFrame(sc.transform(x))\n",
+    "\n",
+    "y = df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2, random_state=1234)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Build Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Set up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop_early = EarlyStopping(monitor='val_loss', patience=3)\n",
+    "\n",
+    "# Final Results\n",
+    "final_models = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_model(model):\n",
+    "    '''\n",
+    "    Describe Model architecture\n",
+    "    '''\n",
+    "    print(f\"Describe models architecture\")\n",
+    "    for i, layer in enumerate(model.layers):\n",
+    "        number_of_units = layer.units if hasattr(layer, 'units') else 0\n",
+    "\n",
+    "        if hasattr(layer, \"activation\"):\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: \", layer.activation)\n",
+    "        else:\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: None\")\n",
+    "\n",
+    "\n",
+    "def get_best_model(tuner):\n",
+    "    '''\n",
+    "    Describe and return the best model found from keras tuner\n",
+    "    '''\n",
+    "    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+    "    best_model = tuner.hypermodel.build(best_hps)\n",
+    "\n",
+    "    describe_model(best_model)\n",
+    "\n",
+    "    print(\"\\nOther params:\")\n",
+    "    ignore_params = [\"tuner\", \"activation\", \"layer\", \"epoch\"]\n",
+    "    for param, value in best_hps.values.items():\n",
+    "        if not any(word in param for word in ignore_params):\n",
+    "            print(f\"{param}: {value}\")\n",
+    "\n",
+    "    return best_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Model with 3 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_3l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(36, input_dim = 36, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 46s]\n",
+      "val_accuracy: 0.9980487823486328\n",
+      "\n",
+      "Best val_accuracy So Far: 0.9980487823486328\n",
+      "Total elapsed time: 00h 08m 25s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_3l = kt.Hyperband(\n",
+    "    model_3l_builder,\n",
+    "    objective='val_accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo',\n",
+    ")\n",
+    "tuner_3l.search(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-2: 448 units, func:  <function tanh at 0x16bf85ee0>\n",
+      "Layer-3: 2 units, func:  <function softmax at 0x16bf85160>\n",
+      "\n",
+      "Other params:\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n",
+      "   5/1230 [..............................] - ETA: 19s - loss: 0.6247 - accuracy: 0.6600 "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 09:54:28.588878: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - ETA: 0s - loss: 0.0504 - accuracy: 0.9848"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 09:54:39.929268: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - 14s 11ms/step - loss: 0.0504 - accuracy: 0.9848 - val_loss: 0.0889 - val_accuracy: 0.9717\n",
+      "Epoch 2/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0241 - accuracy: 0.9940 - val_loss: 0.0188 - val_accuracy: 0.9948\n",
+      "Epoch 3/100\n",
+      "1230/1230 [==============================] - 13s 10ms/step - loss: 0.0187 - accuracy: 0.9946 - val_loss: 0.0127 - val_accuracy: 0.9964\n",
+      "Epoch 4/100\n",
+      "1230/1230 [==============================] - 13s 10ms/step - loss: 0.0179 - accuracy: 0.9950 - val_loss: 0.0140 - val_accuracy: 0.9958\n",
+      "Epoch 5/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0145 - accuracy: 0.9958 - val_loss: 0.0211 - val_accuracy: 0.9958\n",
+      "Epoch 6/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.0093 - val_accuracy: 0.9984\n",
+      "Epoch 7/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.0077 - val_accuracy: 0.9984\n",
+      "Epoch 8/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0112 - val_accuracy: 0.9977\n",
+      "Epoch 9/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0121 - accuracy: 0.9959 - val_loss: 0.0073 - val_accuracy: 0.9984\n",
+      "Epoch 10/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0110 - accuracy: 0.9963 - val_loss: 0.0108 - val_accuracy: 0.9971\n",
+      "Epoch 11/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.0110 - val_accuracy: 0.9971\n",
+      "Epoch 12/100\n",
+      "1230/1230 [==============================] - 13s 11ms/step - loss: 0.0098 - accuracy: 0.9972 - val_loss: 0.0107 - val_accuracy: 0.9967\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x296950eb0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_3l = get_best_model(tuner_3l)\n",
+    "model_3l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"3_layers\"] = model_3l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Model with 5 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_5l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(36, input_dim = 36, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 54s]\n",
+      "val_accuracy: 0.9973983764648438\n",
+      "\n",
+      "Best val_accuracy So Far: 0.9986991882324219\n",
+      "Total elapsed time: 00h 11m 12s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_5l = kt.Hyperband(\n",
+    "    model_5l_builder,\n",
+    "    objective='val_accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_2'\n",
+    ")\n",
+    "tuner_5l.search(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[stop_early, TensorBoard(\"./keras_tuner_dir/logs\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-2: 160 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-3: 352 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-4: 64 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-5: 2 units, func:  <function softmax at 0x16bf85160>\n",
+      "\n",
+      "Other params:\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 10:15:07.538823: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - ETA: 0s - loss: 0.0494 - accuracy: 0.9848"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 10:15:21.397335: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0494 - accuracy: 0.9848 - val_loss: 0.0152 - val_accuracy: 0.9958\n",
+      "Epoch 2/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0238 - accuracy: 0.9932 - val_loss: 0.0145 - val_accuracy: 0.9954\n",
+      "Epoch 3/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0193 - accuracy: 0.9947 - val_loss: 0.0146 - val_accuracy: 0.9971\n",
+      "Epoch 4/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0169 - accuracy: 0.9950 - val_loss: 0.0140 - val_accuracy: 0.9964\n",
+      "Epoch 5/100\n",
+      "1230/1230 [==============================] - 15s 13ms/step - loss: 0.0160 - accuracy: 0.9960 - val_loss: 0.0154 - val_accuracy: 0.9964\n",
+      "Epoch 6/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0135 - accuracy: 0.9963 - val_loss: 0.0126 - val_accuracy: 0.9961\n",
+      "Epoch 7/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0126 - accuracy: 0.9960 - val_loss: 0.0098 - val_accuracy: 0.9971\n",
+      "Epoch 8/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0106 - accuracy: 0.9966 - val_loss: 0.0090 - val_accuracy: 0.9971\n",
+      "Epoch 9/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0110 - accuracy: 0.9966 - val_loss: 0.0146 - val_accuracy: 0.9974\n",
+      "Epoch 10/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0098 - accuracy: 0.9963 - val_loss: 0.0257 - val_accuracy: 0.9922\n",
+      "Epoch 11/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 0.0138 - val_accuracy: 0.9961\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x28a166fa0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_5l = get_best_model(tuner_5l)\n",
+    "model_5l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"5_layers\"] = model_5l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4. Model with 7 layers include Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_7lD_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(36, input_dim = 36, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_dropout_1 = hp.Float('dropout_1', min_value=0.1, max_value=0.5, step=0.1)\n",
+    "    hp_dropout_2 = hp.Float('dropout_2', min_value=0.1, max_value=0.5, step=0.1)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dropout(rate=hp_dropout_1))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dropout(rate=hp_dropout_2))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 01m 04s]\n",
+      "accuracy: 0.9945515394210815\n",
+      "\n",
+      "Best accuracy So Far: 0.9969098567962646\n",
+      "Total elapsed time: 00h 12m 19s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_7lD = kt.Hyperband(\n",
+    "    model_7lD_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_3'\n",
+    ")\n",
+    "tuner_7lD.search(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[stop_early, TensorBoard(\"./keras_tuner_dir/logs\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-2: 320 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 96 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 448 units, func:  <function relu at 0x16bf85b80>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x16bf85160>\n",
+      "\n",
+      "Other params:\n",
+      "dropout_1: 0.30000000000000004\n",
+      "dropout_2: 0.30000000000000004\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 10:37:14.947724: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - ETA: 0s - loss: 0.0592 - accuracy: 0.9811"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 10:37:31.869492: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - 20s 16ms/step - loss: 0.0592 - accuracy: 0.9811 - val_loss: 0.0177 - val_accuracy: 0.9961\n",
+      "Epoch 2/100\n",
+      "1230/1230 [==============================] - 19s 16ms/step - loss: 0.0235 - accuracy: 0.9934 - val_loss: 0.0164 - val_accuracy: 0.9951\n",
+      "Epoch 3/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0206 - accuracy: 0.9945 - val_loss: 0.0150 - val_accuracy: 0.9945\n",
+      "Epoch 4/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0175 - accuracy: 0.9951 - val_loss: 0.0160 - val_accuracy: 0.9961\n",
+      "Epoch 5/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0176 - accuracy: 0.9950 - val_loss: 0.0131 - val_accuracy: 0.9964\n",
+      "Epoch 6/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0137 - accuracy: 0.9960 - val_loss: 0.0091 - val_accuracy: 0.9984\n",
+      "Epoch 7/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0141 - accuracy: 0.9959 - val_loss: 0.0121 - val_accuracy: 0.9958\n",
+      "Epoch 8/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0118 - accuracy: 0.9964 - val_loss: 0.0089 - val_accuracy: 0.9967\n",
+      "Epoch 9/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0097 - accuracy: 0.9969 - val_loss: 0.0155 - val_accuracy: 0.9974\n",
+      "Epoch 10/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0154 - accuracy: 0.9964 - val_loss: 0.0093 - val_accuracy: 0.9974\n",
+      "Epoch 11/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0118 - accuracy: 0.9970 - val_loss: 0.0073 - val_accuracy: 0.9987\n",
+      "Epoch 12/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0103 - accuracy: 0.9971 - val_loss: 0.0185 - val_accuracy: 0.9980\n",
+      "Epoch 13/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0112 - accuracy: 0.9971 - val_loss: 0.0105 - val_accuracy: 0.9977\n",
+      "Epoch 14/100\n",
+      "1230/1230 [==============================] - 19s 15ms/step - loss: 0.0132 - accuracy: 0.9965 - val_loss: 0.0183 - val_accuracy: 0.9964\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x28fcad400>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_7lD = get_best_model(tuner_7lD)\n",
+    "model_7lD.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"7_layers_with_dropout\"] = model_7lD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5. Model with 7 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_7l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(36, input_dim = 36, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_4 = hp.Int('layer_4', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_5 = hp.Int('layer_5', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_4, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_5, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 51s]\n",
+      "val_accuracy: 0.9973983764648438\n",
+      "\n",
+      "Best val_accuracy So Far: 0.9977235794067383\n",
+      "Total elapsed time: 00h 02m 22s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_7l = kt.Hyperband(\n",
+    "    model_7l_builder,\n",
+    "    objective='val_accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_6'\n",
+    ")\n",
+    "tuner_7l.search(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[stop_early, TensorBoard(\"./keras_tuner_dir/logs\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x13a058b80>\n",
+      "Layer-2: 192 units, func:  <function tanh at 0x13a058ee0>\n",
+      "Layer-3: 320 units, func:  <function tanh at 0x13a058ee0>\n",
+      "Layer-4: 448 units, func:  <function tanh at 0x13a058ee0>\n",
+      "Layer-5: 224 units, func:  <function tanh at 0x13a058ee0>\n",
+      "Layer-6: 448 units, func:  <function tanh at 0x13a058ee0>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x13a058160>\n",
+      "\n",
+      "Other params:\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n",
+      "   1/1230 [..............................] - ETA: 8:04 - loss: 0.6561 - accuracy: 0.6000"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 14:29:10.795739: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - ETA: 0s - loss: 0.0720 - accuracy: 0.9748"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 14:29:24.355056: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0720 - accuracy: 0.9748 - val_loss: 0.0219 - val_accuracy: 0.9941\n",
+      "Epoch 2/100\n",
+      "1230/1230 [==============================] - 15s 12ms/step - loss: 0.0319 - accuracy: 0.9910 - val_loss: 0.0353 - val_accuracy: 0.9893\n",
+      "Epoch 3/100\n",
+      "1230/1230 [==============================] - 15s 12ms/step - loss: 0.0262 - accuracy: 0.9927 - val_loss: 0.0149 - val_accuracy: 0.9958\n",
+      "Epoch 4/100\n",
+      "1230/1230 [==============================] - 15s 12ms/step - loss: 0.0221 - accuracy: 0.9936 - val_loss: 0.0125 - val_accuracy: 0.9964\n",
+      "Epoch 5/100\n",
+      "1230/1230 [==============================] - 15s 12ms/step - loss: 0.0195 - accuracy: 0.9943 - val_loss: 0.0171 - val_accuracy: 0.9951\n",
+      "Epoch 6/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0186 - accuracy: 0.9942 - val_loss: 0.0128 - val_accuracy: 0.9971\n",
+      "Epoch 7/100\n",
+      "1230/1230 [==============================] - 16s 13ms/step - loss: 0.0170 - accuracy: 0.9951 - val_loss: 0.0132 - val_accuracy: 0.9974\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x15a0f17c0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_7l = get_best_model(tuner_7l)\n",
+    "model_7l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"7_layers\"] = model_7l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.6. Describe final models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3_layers: Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-2: 448 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-3: 2 units, func:  <function softmax at 0x17fe29160>\n",
+      "\n",
+      "5_layers: Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-2: 160 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-3: 352 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-4: 64 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-5: 2 units, func:  <function softmax at 0x17fe29160>\n",
+      "\n",
+      "7_layers_with_dropout: Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-2: 320 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 96 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 448 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x17fe29160>\n",
+      "\n",
+      "7_layers: Describe models architecture\n",
+      "Layer-1: 36 units, func:  <function relu at 0x17fe29b80>\n",
+      "Layer-2: 192 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-3: 320 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-4: 448 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-5: 224 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-6: 448 units, func:  <function tanh at 0x17fe29ee0>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x17fe29160>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, model in final_models.items():\n",
+    "    print(f\"{name}: \", end=\"\")\n",
+    "    describe_model(model)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Train set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[0.998, 0.997]</td>\n",
+       "      <td>[0.998, 0.997]</td>\n",
+       "      <td>[0.998, 0.997]</td>\n",
+       "      <td>[[1675, 4], [4, 1392]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[0.997, 0.996]</td>\n",
+       "      <td>[0.997, 0.996]</td>\n",
+       "      <td>[0.997, 0.996]</td>\n",
+       "      <td>[[1674, 5], [5, 1391]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[0.998, 0.995]</td>\n",
+       "      <td>[0.996, 0.997]</td>\n",
+       "      <td>[0.997, 0.996]</td>\n",
+       "      <td>[[1672, 7], [4, 1392]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[[1673, 6], [6, 1390]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model Precision Score    Recall Score        F1 score  \\\n",
+       "0               7_layers  [0.998, 0.997]  [0.998, 0.997]  [0.998, 0.997]   \n",
+       "1               3_layers  [0.997, 0.996]  [0.997, 0.996]  [0.997, 0.996]   \n",
+       "2  7_layers_with_dropout  [0.998, 0.995]  [0.996, 0.997]  [0.997, 0.996]   \n",
+       "3               5_layers  [0.996, 0.996]  [0.996, 0.996]  [0.996, 0.996]   \n",
+       "\n",
+       "         Confusion Matrix  \n",
+       "0  [[1675, 4], [4, 1392]]  \n",
+       "1  [[1674, 5], [5, 1391]]  \n",
+       "2  [[1672, 7], [4, 1392]]  \n",
+       "3  [[1673, 6], [6, 1390]]  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(x_test, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    \n",
+    "    train_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "train_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
+    "pd.DataFrame(train_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2. Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 604 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    339\n",
+      "L    265\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "test_df = describe_dataset(\"./test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-25 15:34:28.174069: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:34:28.287586: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:34:28.423087: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:34:28.546100: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[0.944, 1.0]</td>\n",
+       "      <td>[1.0, 0.925]</td>\n",
+       "      <td>[0.971, 0.961]</td>\n",
+       "      <td>[[339, 0], [20, 245]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[0.926, 1.0]</td>\n",
+       "      <td>[1.0, 0.898]</td>\n",
+       "      <td>[0.962, 0.946]</td>\n",
+       "      <td>[[339, 0], [27, 238]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[0.909, 0.963]</td>\n",
+       "      <td>[0.973, 0.875]</td>\n",
+       "      <td>[0.94, 0.917]</td>\n",
+       "      <td>[[330, 9], [33, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[0.896, 0.983]</td>\n",
+       "      <td>[0.988, 0.853]</td>\n",
+       "      <td>[0.94, 0.913]</td>\n",
+       "      <td>[[335, 4], [39, 226]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model Precision Score    Recall Score        F1 score  \\\n",
+       "0               7_layers    [0.944, 1.0]    [1.0, 0.925]  [0.971, 0.961]   \n",
+       "1               5_layers    [0.926, 1.0]    [1.0, 0.898]  [0.962, 0.946]   \n",
+       "2  7_layers_with_dropout  [0.909, 0.963]  [0.973, 0.875]   [0.94, 0.917]   \n",
+       "3               3_layers  [0.896, 0.983]  [0.988, 0.853]   [0.94, 0.913]   \n",
+       "\n",
+       "        Confusion Matrix  \n",
+       "0  [[339, 0], [20, 245]]  \n",
+       "1  [[339, 0], [27, 238]]  \n",
+       "2  [[330, 9], [33, 232]]  \n",
+       "3  [[335, 4], [39, 226]]  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    \n",
+    "    test_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "test_set_results.sort(key=lambda k: k[1] + k[2] + k[3], reverse=True)\n",
+    "pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Dumped Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://4145d713-d810-484c-b518-b4ae694e4919/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump the best model to a pickle file\n",
+    "with open(\"./model/bicep_dp.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models[\"7_layers\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://5ccb0e7f-b3f8-4602-9b3c-2ae89d1a2d69/assets\n",
+      "INFO:tensorflow:Assets written to: ram://5d2d95b4-ff82-487d-bd98-ba859e8eced0/assets\n",
+      "INFO:tensorflow:Assets written to: ram://557449b4-6368-4822-a75f-79675a055ab9/assets\n",
+      "INFO:tensorflow:Assets written to: ram://4857368e-b747-43dd-9b2d-6e986317f4b8/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump final results\n",
+    "with open(\"./model/all_models.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/bicep_model/4.evaluation.ipynb b/core/bicep_model/4.evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a470ad3e5b1fdd5a3a414c3146500c3945bfbc7
--- /dev/null
+++ b/core/bicep_model/4.evaluation.ipynb
@@ -0,0 +1,912 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[61196]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10ab48860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece480). One of the two will be used. Which one is undefined.\n",
+      "objc[61196]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105918a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[61196]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105918a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[61196]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105918ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix, roc_curve, auc\n",
+    "\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Set ups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Load models & scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-28 10:16:34.709228: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2022-11-28 10:16:34.709336: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load all sklearn models\n",
+    "with open(\"./model/all_sklearn.pkl\", \"rb\") as f:\n",
+    "    sklearn_models = pickle.load(f)\n",
+    "\n",
+    "# Load all deep learning models\n",
+    "with open(\"./model/all_dp.pkl\", \"rb\") as f:\n",
+    "    dp_models = pickle.load(f)\n",
+    "\n",
+    "# Load input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Process Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v']\n",
+      "Number of rows: 604 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "C    339\n",
+      "L    265\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "test_df = describe_dataset(\"./test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 1\n",
+    "\n",
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"].astype('int')\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Test set evaluation for all models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Sklearn models evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.792694</td>\n",
+       "      <td>0.737775</td>\n",
+       "      <td>0.761589</td>\n",
+       "      <td>0.740550</td>\n",
+       "      <td>[[316, 23], [121, 144]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.929966</td>\n",
+       "      <td>0.933762</td>\n",
+       "      <td>0.932119</td>\n",
+       "      <td>0.931420</td>\n",
+       "      <td>[[312, 27], [14, 251]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.975401</td>\n",
+       "      <td>0.968336</td>\n",
+       "      <td>0.971854</td>\n",
+       "      <td>0.971230</td>\n",
+       "      <td>[[338, 1], [16, 249]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.684255</td>\n",
+       "      <td>0.650782</td>\n",
+       "      <td>0.675497</td>\n",
+       "      <td>0.647579</td>\n",
+       "      <td>[[289, 50], [146, 119]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.712457</td>\n",
+       "      <td>0.715011</td>\n",
+       "      <td>0.715232</td>\n",
+       "      <td>0.712937</td>\n",
+       "      <td>[[243, 96], [76, 189]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.797368</td>\n",
+       "      <td>0.564151</td>\n",
+       "      <td>0.617550</td>\n",
+       "      <td>0.486650</td>\n",
+       "      <td>[[339, 0], [231, 34]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.947230</td>\n",
+       "      <td>0.924528</td>\n",
+       "      <td>0.933775</td>\n",
+       "      <td>0.931329</td>\n",
+       "      <td>[[339, 0], [40, 225]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model  Precision Score  Recall Score  Accuracy Score  F1 Score  \\\n",
+       "0    LR         0.792694      0.737775        0.761589  0.740550   \n",
+       "1   SVC         0.929966      0.933762        0.932119  0.931420   \n",
+       "2   KNN         0.975401      0.968336        0.971854  0.971230   \n",
+       "3   DTC         0.684255      0.650782        0.675497  0.647579   \n",
+       "4  SGDC         0.712457      0.715011        0.715232  0.712937   \n",
+       "5    NB         0.797368      0.564151        0.617550  0.486650   \n",
+       "6    RF         0.947230      0.924528        0.933775  0.931329   \n",
+       "\n",
+       "          Confusion Matrix  \n",
+       "0  [[316, 23], [121, 144]]  \n",
+       "1   [[312, 27], [14, 251]]  \n",
+       "2    [[338, 1], [16, 249]]  \n",
+       "3  [[289, 50], [146, 119]]  \n",
+       "4   [[243, 96], [76, 189]]  \n",
+       "5    [[339, 0], [231, 34]]  \n",
+       "6    [[339, 0], [40, 225]]  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in sklearn_models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=\"macro\")\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=\"macro\")\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=\"macro\")\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[0, 1])\n",
+    "    testset_final_results.append(( name,  p_score, r_score, a_score, f1_score_result, cm ))\n",
+    "\n",
+    "\n",
+    "sklearn_eval = pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"Accuracy Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "sklearn_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Deep learning models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.939165</td>\n",
+       "      <td>0.920515</td>\n",
+       "      <td>0.928808</td>\n",
+       "      <td>0.926411</td>\n",
+       "      <td>[[335, 4], [39, 226]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.963115</td>\n",
+       "      <td>0.949057</td>\n",
+       "      <td>0.955298</td>\n",
+       "      <td>0.954012</td>\n",
+       "      <td>[[339, 0], [27, 238]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.935873</td>\n",
+       "      <td>0.924462</td>\n",
+       "      <td>0.930464</td>\n",
+       "      <td>0.928583</td>\n",
+       "      <td>[[330, 9], [33, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.972145</td>\n",
+       "      <td>0.962264</td>\n",
+       "      <td>0.966887</td>\n",
+       "      <td>0.966066</td>\n",
+       "      <td>[[339, 0], [20, 245]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model  Precision Score  Recall Score  Accuracy Score  \\\n",
+       "0               3_layers         0.939165      0.920515        0.928808   \n",
+       "1               5_layers         0.963115      0.949057        0.955298   \n",
+       "2  7_layers_with_dropout         0.935873      0.924462        0.930464   \n",
+       "3               7_layers         0.972145      0.962264        0.966887   \n",
+       "\n",
+       "   F1 Score       Confusion Matrix  \n",
+       "0  0.926411  [[335, 4], [39, 226]]  \n",
+       "1  0.954012  [[339, 0], [27, 238]]  \n",
+       "2  0.928583  [[330, 9], [33, 232]]  \n",
+       "3  0.966066  [[339, 0], [20, 245]]  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in dp_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    p_score = precision_score(y_test_class, y_pred_class, average=\"macro\")\n",
+    "    a_score = accuracy_score(y_test_class, y_pred_class)\n",
+    "    r_score = recall_score(y_test_class, y_pred_class, average=\"macro\")\n",
+    "    f1_score_result = f1_score(y_test_class, y_pred_class, average=\"macro\")\n",
+    "    \n",
+    "    test_set_results.append(( name,  (p_score), r_score, (a_score), (f1_score_result), cm ))\n",
+    "\n",
+    "dp_eval = pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"Accuracy Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "dp_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Final Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.975401</td>\n",
+       "      <td>0.968336</td>\n",
+       "      <td>0.971854</td>\n",
+       "      <td>0.971230</td>\n",
+       "      <td>[[338, 1], [16, 249]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.972145</td>\n",
+       "      <td>0.962264</td>\n",
+       "      <td>0.966887</td>\n",
+       "      <td>0.966066</td>\n",
+       "      <td>[[339, 0], [20, 245]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.963115</td>\n",
+       "      <td>0.949057</td>\n",
+       "      <td>0.955298</td>\n",
+       "      <td>0.954012</td>\n",
+       "      <td>[[339, 0], [27, 238]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.929966</td>\n",
+       "      <td>0.933762</td>\n",
+       "      <td>0.932119</td>\n",
+       "      <td>0.931420</td>\n",
+       "      <td>[[312, 27], [14, 251]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.947230</td>\n",
+       "      <td>0.924528</td>\n",
+       "      <td>0.933775</td>\n",
+       "      <td>0.931329</td>\n",
+       "      <td>[[339, 0], [40, 225]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.935873</td>\n",
+       "      <td>0.924462</td>\n",
+       "      <td>0.930464</td>\n",
+       "      <td>0.928583</td>\n",
+       "      <td>[[330, 9], [33, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.939165</td>\n",
+       "      <td>0.920515</td>\n",
+       "      <td>0.928808</td>\n",
+       "      <td>0.926411</td>\n",
+       "      <td>[[335, 4], [39, 226]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.792694</td>\n",
+       "      <td>0.737775</td>\n",
+       "      <td>0.761589</td>\n",
+       "      <td>0.740550</td>\n",
+       "      <td>[[316, 23], [121, 144]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.712457</td>\n",
+       "      <td>0.715011</td>\n",
+       "      <td>0.715232</td>\n",
+       "      <td>0.712937</td>\n",
+       "      <td>[[243, 96], [76, 189]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.684255</td>\n",
+       "      <td>0.650782</td>\n",
+       "      <td>0.675497</td>\n",
+       "      <td>0.647579</td>\n",
+       "      <td>[[289, 50], [146, 119]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.797368</td>\n",
+       "      <td>0.564151</td>\n",
+       "      <td>0.617550</td>\n",
+       "      <td>0.486650</td>\n",
+       "      <td>[[339, 0], [231, 34]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Model  Precision Score  Recall Score  Accuracy Score  \\\n",
+       "0                     KNN         0.975401      0.968336        0.971854   \n",
+       "1                7_layers         0.972145      0.962264        0.966887   \n",
+       "2                5_layers         0.963115      0.949057        0.955298   \n",
+       "3                     SVC         0.929966      0.933762        0.932119   \n",
+       "4                      RF         0.947230      0.924528        0.933775   \n",
+       "5   7_layers_with_dropout         0.935873      0.924462        0.930464   \n",
+       "6                3_layers         0.939165      0.920515        0.928808   \n",
+       "7                      LR         0.792694      0.737775        0.761589   \n",
+       "8                    SGDC         0.712457      0.715011        0.715232   \n",
+       "9                     DTC         0.684255      0.650782        0.675497   \n",
+       "10                     NB         0.797368      0.564151        0.617550   \n",
+       "\n",
+       "    F1 Score         Confusion Matrix  \n",
+       "0   0.971230    [[338, 1], [16, 249]]  \n",
+       "1   0.966066    [[339, 0], [20, 245]]  \n",
+       "2   0.954012    [[339, 0], [27, 238]]  \n",
+       "3   0.931420   [[312, 27], [14, 251]]  \n",
+       "4   0.931329    [[339, 0], [40, 225]]  \n",
+       "5   0.928583    [[330, 9], [33, 232]]  \n",
+       "6   0.926411    [[335, 4], [39, 226]]  \n",
+       "7   0.740550  [[316, 23], [121, 144]]  \n",
+       "8   0.712937   [[243, 96], [76, 189]]  \n",
+       "9   0.647579  [[289, 50], [146, 119]]  \n",
+       "10  0.486650    [[339, 0], [231, 34]]  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_df = pd.concat([sklearn_eval, dp_eval])\n",
+    "eval_df = eval_df.sort_values(by=['F1 Score'], ascending=False).reset_index(drop=True)\n",
+    "eval_df.to_csv(f\"evaluation.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "eval_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Best model - ROC - Confusion Matrix\n",
+    "\n",
+    "As we can see from the evaluation, the best model according to the F1 Score is the KNN model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.95480226, 0.996     ]),\n",
+       " array([0.99705015, 0.93962264]),\n",
+       " array([0.97546898, 0.96699029]))"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model = sklearn_models[\"KNN\"]\n",
+    "y_predictions = best_model.predict(test_x)\n",
+    "\n",
+    "p_score = precision_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "r_score = recall_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "f1_score_result = f1_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "\n",
+    "p_score, r_score, f1_score_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.971"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0.975 + 0.967) / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "KNN_cm = eval_df[ eval_df[\"Model\"] == 'KNN' ][\"Confusion Matrix\"].values[0]\n",
+    "\n",
+    "cm_array_df = pd.DataFrame(KNN_cm, index=[\"C\", \"L\"], columns=[\"C\", \"L\"])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,4)) \n",
+    "sns.heatmap(cm_array_df, linewidths=1, annot=True, ax=ax, fmt='g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. F1 Score and Confidence correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_labels(y_pred, y_pred_proba, threshold):\n",
+    "    '''Return prediction taking confidence threshold into account'''\n",
+    "    results = []\n",
+    "\n",
+    "    for index, predicted_class in enumerate(y_pred):\n",
+    "        prediction_probabilities = y_pred_proba[index]\n",
+    "        class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
+    "\n",
+    "        results.append(predicted_class if class_prediction_probability >= threshold else -1)\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def calculate_correlation_score_confidence(test_x, test_y):\n",
+    "    '''Calculate correlation between Precision score/Recall score/F1 score and confidence threshold'''\n",
+    "    y_predictions = best_model.predict(test_x)\n",
+    "    y_predict_proba = best_model.predict_proba(test_x)\n",
+    "\n",
+    "    thresholds = list(np.arange(0, 1.01, 0.01))\n",
+    "\n",
+    "    f1_score_results = []\n",
+    "\n",
+    "    for threshold in thresholds:\n",
+    "        true_predictions = to_labels(y_predictions, y_predict_proba, threshold)\n",
+    "        f1_s = list(f1_score(test_y, true_predictions, labels=[0, 1], average=None))\n",
+    "        all_class_f1 = f1_score(test_y, true_predictions, labels=[0, 1, 2], average=\"weighted\")\n",
+    "        f1_s.append(all_class_f1)\n",
+    "        f1_score_results.append(f1_s)\n",
+    "    \n",
+    "    return thresholds, f1_score_results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thresholds, f1_scores = calculate_correlation_score_confidence(test_x, test_y)\n",
+    "\n",
+    "first_class = [ el[0] for el in f1_scores ]\n",
+    "second_class = [ el[1] for el in f1_scores ]\n",
+    "all_classes = [ el[2] for el in f1_scores ]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "plt.plot(thresholds, first_class, label = \"F1 Score - Correct class\")\n",
+    "plt.plot(thresholds, second_class, label = \"F1 Score - Incorrect class\")\n",
+    "plt.plot(thresholds, all_classes, label = \"F1 Score - All classes\", linewidth=2.0, color=\"blue\")\n",
+    "plt.legend(loc = 'lower left')\n",
+    "plt.ylim([0.8, 1])\n",
+    "plt.xlim([0.025, 1])\n",
+    "plt.xlabel(\"Threshold\", fontsize = 12)\n",
+    "plt.ylabel(\"Score\", fontsize = 12)\n",
+    "# plt.axvline(thresholds[np.argmin(abs(precision-recall))], color=\"k\", ls = \"--\")\n",
+    "# plt.title(label = F\"Threshold = {thresholds[np.argmin(abs(precision-recall))]:.3f}\", fontsize = 12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal Threshold: 0.6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# calculate the fpr and tpr for all thresholds of the classification\n",
+    "probs = best_model.predict_proba(test_x)\n",
+    "preds = probs[:,1]\n",
+    "fpr, tpr, threshold = roc_curve(test_y, preds)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "optimal_idx = np.argmax(tpr - fpr)\n",
+    "optimal_threshold = threshold[optimal_idx]\n",
+    "print(f\"Optimal Threshold: {optimal_threshold}\")\n",
+    "\n",
+    "# method I: plt\n",
+    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
+    "plt.plot([0, 1], [0, 1],'r--', label=\"Random Classifier\")\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/bicep_model/5.detection.ipynb b/core/bicep_model/5.detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4a1f03af1b554f14a536da066fbc3ceaeb6966ed
--- /dev/null
+++ b/core/bicep_model/5.detection.ipynb
@@ -0,0 +1,619 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Set up important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame from OpenCV to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def save_frame_as_image(frame, message: str = None):\n",
+    "    '''\n",
+    "    Save a frame as image to display the error\n",
+    "    '''\n",
+    "    now = datetime.datetime.now()\n",
+    "\n",
+    "    if message:\n",
+    "        cv2.putText(frame, message, (50, 150), cv2.FONT_HERSHEY_COMPLEX, 0.4, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "        \n",
+    "    print(\"Saving ...\")\n",
+    "    cv2.imwrite(f\"../data/logs/bicep_{now}.jpg\", frame)\n",
+    "\n",
+    "\n",
+    "def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
+    "    '''\n",
+    "    Calculate the angle between 3 points\n",
+    "    Unit of the angle will be in Degree\n",
+    "    '''\n",
+    "    point1 = np.array(point1)\n",
+    "    point2 = np.array(point2)\n",
+    "    point3 = np.array(point3)\n",
+    "\n",
+    "    # Calculate algo\n",
+    "    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
+    "    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
+    "\n",
+    "    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
+    "    return angleInDeg\n",
+    "\n",
+    "\n",
+    "def extract_important_keypoints(results, important_landmarks: list) -> list:\n",
+    "    '''\n",
+    "    Extract important keypoints from mediapipe pose detection\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "    data = []\n",
+    "    for lm in important_landmarks:\n",
+    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "    \n",
+    "    return np.array(data).flatten().tolist()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. OOP method for Analyze Pose of each arm\n",
+    "\n",
+    "To be easier to detect both arm at the same time, I choose to do the calculation for bicep counter and error detection with OOP.\n",
+    "\n",
+    "*Note: Every If any joints from an arm is appeared to be in poor visibility according to mediapipe, that arm will be skip*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BicepPoseAnalysis:\n",
+    "    def __init__(self, side: str, stage_down_threshold: float, stage_up_threshold: float, peak_contraction_threshold: float, loose_upper_arm_angle_threshold: float, visibility_threshold: float):\n",
+    "        # Initialize thresholds\n",
+    "        self.stage_down_threshold = stage_down_threshold\n",
+    "        self.stage_up_threshold = stage_up_threshold\n",
+    "        self.peak_contraction_threshold = peak_contraction_threshold\n",
+    "        self.loose_upper_arm_angle_threshold = loose_upper_arm_angle_threshold\n",
+    "        self.visibility_threshold = visibility_threshold\n",
+    "\n",
+    "        self.side = side\n",
+    "        self.counter = 0\n",
+    "        self.stage = \"down\"\n",
+    "        self.is_visible = True\n",
+    "        self.detected_errors = {\n",
+    "            \"LOOSE_UPPER_ARM\": 0,\n",
+    "            \"PEAK_CONTRACTION\": 0,\n",
+    "        }\n",
+    "\n",
+    "        # Params for loose upper arm error detection\n",
+    "        self.loose_upper_arm = False\n",
+    "\n",
+    "        # Params for peak contraction error detection\n",
+    "        self.peak_contraction_angle = 1000\n",
+    "        self.peak_contraction_frame = None\n",
+    "    \n",
+    "    def get_joints(self, landmarks) -> bool:\n",
+    "        '''\n",
+    "        Check for joints' visibility then get joints coordinate\n",
+    "        '''\n",
+    "        side = self.side.upper()\n",
+    "\n",
+    "        # Check visibility\n",
+    "        joints_visibility = [ landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].visibility, landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].visibility, landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].visibility ]\n",
+    "\n",
+    "        is_visible = all([ vis > self.visibility_threshold for vis in joints_visibility ])\n",
+    "        self.is_visible = is_visible\n",
+    "\n",
+    "        if not is_visible:\n",
+    "            return self.is_visible\n",
+    "        \n",
+    "        # Get joints' coordinates\n",
+    "        self.shoulder = [ landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].y ]\n",
+    "        self.elbow = [ landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].y ]\n",
+    "        self.wrist = [ landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].y ]\n",
+    "\n",
+    "        return self.is_visible\n",
+    "    \n",
+    "    def analyze_pose(self, landmarks, frame):\n",
+    "        '''\n",
+    "        - Bicep Counter\n",
+    "        - Errors Detection\n",
+    "        '''\n",
+    "        self.get_joints(landmarks)\n",
+    "\n",
+    "        # Cancel calculation if visibility is poor\n",
+    "        if not self.is_visible:\n",
+    "            return (None, None)\n",
+    "\n",
+    "        # * Calculate curl angle for counter\n",
+    "        bicep_curl_angle = int(calculate_angle(self.shoulder, self.elbow, self.wrist))\n",
+    "        if bicep_curl_angle > self.stage_down_threshold:\n",
+    "            self.stage = \"down\"\n",
+    "        elif bicep_curl_angle < self.stage_up_threshold and self.stage == \"down\":\n",
+    "            self.stage = \"up\"\n",
+    "            self.counter += 1\n",
+    "        \n",
+    "        # * Calculate the angle between the upper arm (shoulder & joint) and the Y axis\n",
+    "        shoulder_projection = [ self.shoulder[0], 1 ] # Represent the projection of the shoulder to the X axis\n",
+    "        ground_upper_arm_angle = int(calculate_angle(self.elbow, self.shoulder, shoulder_projection))\n",
+    "\n",
+    "        # * Evaluation for LOOSE UPPER ARM error\n",
+    "        if ground_upper_arm_angle > self.loose_upper_arm_angle_threshold:\n",
+    "            # Limit the saved frame\n",
+    "            if not self.loose_upper_arm:\n",
+    "                self.loose_upper_arm = True\n",
+    "                # save_frame_as_image(frame, f\"Loose upper arm: {ground_upper_arm_angle}\")\n",
+    "                self.detected_errors[\"LOOSE_UPPER_ARM\"] += 1\n",
+    "        else:\n",
+    "            self.loose_upper_arm = False\n",
+    "        \n",
+    "        # * Evaluate PEAK CONTRACTION error\n",
+    "        if self.stage == \"up\" and bicep_curl_angle < self.peak_contraction_angle:\n",
+    "            # Save peaked contraction every rep\n",
+    "            self.peak_contraction_angle = bicep_curl_angle\n",
+    "            self.peak_contraction_frame = frame\n",
+    "            \n",
+    "        elif self.stage == \"down\":\n",
+    "            # * Evaluate if the peak is higher than the threshold if True, marked as an error then saved that frame\n",
+    "            if self.peak_contraction_angle != 1000 and self.peak_contraction_angle >= self.peak_contraction_threshold:\n",
+    "                # save_frame_as_image(self.peak_contraction_frame, f\"{self.side} - Peak Contraction: {self.peak_contraction_angle}\")\n",
+    "                self.detected_errors[\"PEAK_CONTRACTION\"] += 1\n",
+    "            \n",
+    "            # Reset params\n",
+    "            self.peak_contraction_angle = 1000\n",
+    "            self.peak_contraction_frame = None\n",
+    "        \n",
+    "        return (bicep_curl_angle, ground_upper_arm_angle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Bicep Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VIDEO_PATH1 = \"../data/db_curl/bc_test_1.mp4\"\n",
+    "VIDEO_PATH2 = \"../data/db_curl/bc_test_2.mp4\"\n",
+    "VIDEO_PATH3 = \"../data/db_curl/bc_test_3.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    input_scaler = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1. Detection with SKLearn model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/KNN_model.pkl\", \"rb\") as f:\n",
+    "    sklearn_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH3)\n",
+    "\n",
+    "VISIBILITY_THRESHOLD = 0.65\n",
+    "\n",
+    "# Params for counter\n",
+    "STAGE_UP_THRESHOLD = 90\n",
+    "STAGE_DOWN_THRESHOLD = 120\n",
+    "\n",
+    "# Params to catch FULL RANGE OF MOTION error\n",
+    "PEAK_CONTRACTION_THRESHOLD = 60\n",
+    "\n",
+    "# LOOSE UPPER ARM error detection\n",
+    "LOOSE_UPPER_ARM = False\n",
+    "LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40\n",
+    "\n",
+    "# STANDING POSTURE error detection\n",
+    "POSTURE_ERROR_THRESHOLD = 0.7\n",
+    "posture = \"C\"\n",
+    "\n",
+    "# Init analysis class\n",
+    "left_arm_analysis = BicepPoseAnalysis(side=\"left\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
+    "\n",
+    "right_arm_analysis = BicepPoseAnalysis(side=\"right\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.8, min_tracking_confidence=0.8) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "        \n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            landmarks = results.pose_landmarks.landmark\n",
+    "            \n",
+    "            (left_bicep_curl_angle, left_ground_upper_arm_angle) = left_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
+    "            (right_bicep_curl_angle, right_ground_upper_arm_angle) = right_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
+    "\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results, IMPORTANT_LMS)\n",
+    "            X = pd.DataFrame([row], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            predicted_class = sklearn_model.predict(X)[0]\n",
+    "            prediction_probabilities = sklearn_model.predict_proba(X)[0]\n",
+    "            class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
+    "\n",
+    "            if class_prediction_probability >= POSTURE_ERROR_THRESHOLD:\n",
+    "                posture = predicted_class\n",
+    "\n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (500, 40), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display probability\n",
+    "            cv2.putText(image, \"RIGHT\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.counter) if right_arm_analysis.is_visible else \"UNK\", (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display Left Counter\n",
+    "            cv2.putText(image, \"LEFT\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.counter) if left_arm_analysis.is_visible else \"UNK\", (100, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # * Display error\n",
+    "            # Right arm error\n",
+    "            cv2.putText(image, \"R_PC\", (165, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (160, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "            cv2.putText(image, \"R_LUA\", (225, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (220, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Left arm error\n",
+    "            cv2.putText(image, \"L_PC\", (300, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (295, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "            cv2.putText(image, \"L_LUA\", (380, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (375, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Lean back error\n",
+    "            cv2.putText(image, \"LB\", (460, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(f\"{posture}, {predicted_class}, {class_prediction_probability}\"), (440, 30), cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "\n",
+    "            # * Visualize angles\n",
+    "            # Visualize LEFT arm calculated angles\n",
+    "            if left_arm_analysis.is_visible:\n",
+    "                cv2.putText(image, str(left_bicep_curl_angle), tuple(np.multiply(left_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "                cv2.putText(image, str(left_ground_upper_arm_angle), tuple(np.multiply(left_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "\n",
+    "            # Visualize RIGHT arm calculated angles\n",
+    "            if right_arm_analysis.is_visible:\n",
+    "                cv2.putText(image, str(right_bicep_curl_angle), tuple(np.multiply(right_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
+    "                cv2.putText(image, str(right_ground_upper_arm_angle), tuple(np.multiply(right_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
+    "        \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # if left_arm_analysis.loose_upper_arm:\n",
+    "        #     save_frame_as_image(image, \"\")\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2. Detection with Deep Learning model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/bicep_model_deep_learning.pkl\", \"rb\") as f:\n",
+    "    DL_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH3)\n",
+    "\n",
+    "VISIBILITY_THRESHOLD = 0.65\n",
+    "\n",
+    "# Params for counter\n",
+    "STAGE_UP_THRESHOLD = 90\n",
+    "STAGE_DOWN_THRESHOLD = 120\n",
+    "\n",
+    "# Params to catch FULL RANGE OF MOTION error\n",
+    "PEAK_CONTRACTION_THRESHOLD = 60\n",
+    "\n",
+    "# LOOSE UPPER ARM error detection\n",
+    "LOOSE_UPPER_ARM = False\n",
+    "LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40\n",
+    "\n",
+    "# STANDING POSTURE error detection\n",
+    "POSTURE_ERROR_THRESHOLD = 0.95\n",
+    "posture = 0\n",
+    "\n",
+    "# Init analysis class\n",
+    "left_arm_analysis = BicepPoseAnalysis(side=\"left\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
+    "\n",
+    "right_arm_analysis = BicepPoseAnalysis(side=\"right\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "        \n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            landmarks = results.pose_landmarks.landmark\n",
+    "            \n",
+    "            (left_bicep_curl_angle, left_ground_upper_arm_angle) = left_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
+    "            (right_bicep_curl_angle, right_ground_upper_arm_angle) = right_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
+    "\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results, IMPORTANT_LMS)\n",
+    "            X = pd.DataFrame([row, ], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            prediction = DL_model.predict(X)\n",
+    "            predicted_class = np.argmax(prediction, axis=1)[0]\n",
+    "            prediction_probability = round(max(prediction.tolist()[0]), 2)\n",
+    "\n",
+    "            if prediction_probability >= POSTURE_ERROR_THRESHOLD:\n",
+    "                posture = predicted_class\n",
+    "\n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (500, 40), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display probability\n",
+    "            cv2.putText(image, \"RIGHT\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.counter) if right_arm_analysis.is_visible else \"UNK\", (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display Left Counter\n",
+    "            cv2.putText(image, \"LEFT\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.counter) if left_arm_analysis.is_visible else \"UNK\", (100, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # * Display error\n",
+    "            # Right arm error\n",
+    "            cv2.putText(image, \"R_PC\", (165, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (160, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "            cv2.putText(image, \"R_LUA\", (225, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(right_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (220, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Left arm error\n",
+    "            cv2.putText(image, \"L_PC\", (300, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (295, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "            cv2.putText(image, \"L_LUA\", (380, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(left_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (375, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Lean back error\n",
+    "            cv2.putText(image, \"LB\", (460, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(\"C\" if posture == 0 else \"L\") + f\" ,{predicted_class}, {prediction_probability}\", (440, 30), cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "\n",
+    "            # * Visualize angles\n",
+    "            # Visualize LEFT arm calculated angles\n",
+    "            if left_arm_analysis.is_visible:\n",
+    "                cv2.putText(image, str(left_bicep_curl_angle), tuple(np.multiply(left_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "                cv2.putText(image, str(left_ground_upper_arm_angle), tuple(np.multiply(left_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "\n",
+    "            # Visualize RIGHT arm calculated angles\n",
+    "            if right_arm_analysis.is_visible:\n",
+    "                cv2.putText(image, str(right_bicep_curl_angle), tuple(np.multiply(right_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
+    "                cv2.putText(image, str(right_ground_upper_arm_angle), tuple(np.multiply(right_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
+    "        \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/bicep_model/README.md b/core/bicep_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4cc230c64eb777403f424bfa161d92a356cf01e
--- /dev/null
+++ b/core/bicep_model/README.md
@@ -0,0 +1,36 @@
+<h2 align="center">BICEP CURL MODEL TRAINING PROCESS</h2>
+
+### 1. Folder structure
+
+```
+bicep_model
+│   1.data.ipynb - process collected videos
+|   2.sklearn.ipynb - train models using Sklearn ML algo
+│   3.deep_leaning.ipynb - train models using Deep Learning
+│   4.evaluation.ipynb - evaluate trained models
+│   5.detection.ipynb - detection on test videos
+|   train.csv - train dataset after converted from videos
+|   test.csv - test dataset after converted from videos
+|   evaluation.csv - models' evaluation results
+│
+└───model/ - folder contains best trained models and input scaler
+│   │
+```
+
+### 2. Important landmarks
+
+There are 3 popular errors of bicep curl that will be targeted in this thesis:
+
+-   Loose upper arm: when an arm moves upward during the exercise, the upper arm is moving instead of staying still.
+-   Weak peak contraction: when an arm moves upward, it does not go high enough therefore not put enough contraction to the bicep.
+-   Lean too far back: the performer’s torso leans back and fore during the exercise for momentum.
+
+In my research and exploration, **_the important MediaPipe Pose landmarks_** for this exercise are: nose, left shoulder, right shoulder, right elbow, left elbow, right wrist, left wrist, right hip and left hip.
+
+### 3. Error detection method
+
+1. **Loose upper arm**: Can be detected by calculating the angle between the elbow, shoulder and the shoulder’s projection on the ground. Through my research, if the angle is over 40 degrees, the movement will be classified as a “loose upper arm” error
+
+1. **Weak peak contraction**: Can be detected by calculating the angle between the wrist, elbow and shoulder when the performer’s arm is coming up. Through my research, if the angle is more than 60 degrees before the arm comes down, the movement will be classified as a “weak peak contraction” error.
+
+1. **Lean too far back**: Due to its complexity, machine learning will be used for this detection. See this [notebook](./4.evaluation.ipynb) for a evaluation process for this model.
diff --git a/core/bicep_model/evaluation.csv b/core/bicep_model/evaluation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..683756b92d1bfb99169f73508169eb14bd78d7f2
--- /dev/null
+++ b/core/bicep_model/evaluation.csv
@@ -0,0 +1,23 @@
+Model,Precision Score,Recall Score,Accuracy Score,F1 Score,Confusion Matrix
+KNN,0.9754011299435028,0.9683363945010297,0.9718543046357616,0.9712296333655557,"[[338   1]
+ [ 16 249]]"
+7_layers,0.9721448467966574,0.9622641509433962,0.9668874172185431,0.9660655092982752,"[[339   0]
+ [ 20 245]]"
+5_layers,0.9631147540983607,0.9490566037735849,0.9552980132450332,0.9540120976270039,"[[339   0]
+ [ 27 238]]"
+SVC,0.9299664562828265,0.9337618968108199,0.9321192052980133,0.9314197094947314,"[[312  27]
+ [ 14 251]]"
+RF,0.9472295514511873,0.9245283018867925,0.9337748344370861,0.9313285202660452,"[[339   0]
+ [ 40 225]]"
+7_layers_with_dropout,0.9358732553753301,0.9244615127734179,0.9304635761589404,0.9285834938008851,"[[330   9]
+ [ 33 232]]"
+3_layers,0.9391653103929318,0.9205153893248734,0.9288079470198676,0.9264113788657968,"[[335   4]
+ [ 39 226]]"
+LR,0.7926937886241248,0.737774809372739,0.7615894039735099,0.7405498281786941,"[[316  23]
+ [121 144]]"
+SGDC,0.7124566903151295,0.7150108532309234,0.7152317880794702,0.7129372754904669,"[[243  96]
+ [ 76 189]]"
+DTC,0.6842549139631369,0.6507819892024267,0.6754966887417219,0.6475785613069935,"[[289  50]
+ [146 119]]"
+NB,0.7973684210526315,0.5641509433962264,0.6175496688741722,0.48664966831131273,"[[339   0]
+ [231  34]]"
diff --git a/core/bicep_model/model/KNN_model.pkl b/core/bicep_model/model/KNN_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..7edd4fa5fe17e0be016e9b3f574a217883d79bf2
--- /dev/null
+++ b/core/bicep_model/model/KNN_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 3640626
diff --git a/core/bicep_model/model/RF_model.pkl b/core/bicep_model/model/RF_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..ca956e75fb1e618919b836d7e6fd15b1991db185
--- /dev/null
+++ b/core/bicep_model/model/RF_model.pkl
@@ -0,0 +1,3 @@
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diff --git a/core/bicep_model/model/all_dp.pkl b/core/bicep_model/model/all_dp.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..358b28b733385c8342d1d190bfd3cdc1d3a76720
--- /dev/null
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diff --git a/core/bicep_model/model/all_sklearn.pkl b/core/bicep_model/model/all_sklearn.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..fdce950f35a309ba129c8a40f14ef74f9aa5d121
--- /dev/null
+++ b/core/bicep_model/model/all_sklearn.pkl
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diff --git a/core/bicep_model/model/bicep_dp.pkl b/core/bicep_model/model/bicep_dp.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..13d4870ddd80c2673c53b8aeaf1f6498d176f88b
--- /dev/null
+++ b/core/bicep_model/model/bicep_dp.pkl
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diff --git a/core/bicep_model/model/input_scaler.pkl b/core/bicep_model/model/input_scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..49df696dfd01be41206f2ce079fcd0be0ca11dbe
--- /dev/null
+++ b/core/bicep_model/model/input_scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/core/bicep_model/test.csv b/core/bicep_model/test.csv
new file mode 100644
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--- /dev/null
+++ b/core/bicep_model/test.csv
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diff --git a/core/bicep_model/train.csv b/core/bicep_model/train.csv
new file mode 100644
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--- /dev/null
+++ b/core/bicep_model/train.csv
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+version https://git-lfs.github.com/spec/v1
+oid sha256:12d56e9215045a56103f80a83e1952d70320a25c4cf0a33a898b38aade151e5b
+size 10821608
diff --git a/core/lunge_model/1.stage.data.ipynb b/core/lunge_model/1.stage.data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d7ceffdf17383129af9e45e2744a25069f30a9a7
--- /dev/null
+++ b/core/lunge_model/1.stage.data.ipynb
@@ -0,0 +1,856 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[30090]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x105748860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x281476480). One of the two will be used. Which one is undefined.\n",
+      "objc[30090]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103b10a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2814764d0). One of the two will be used. Which one is undefined.\n",
+      "objc[30090]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103b10a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2814764f8). One of the two will be used. Which one is undefined.\n",
+      "objc[30090]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103b10ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x281476520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "import os\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Build dataset from collected video"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Determine important landmarks for lunge and set up important functions\n",
+    "\n",
+    "There are 3 stages for every lunge exercise\n",
+    "- INIT (I): stand straight\n",
+    "- MID (M): Before go down \n",
+    "- DOWN (D): Go down"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "    \n",
+    "\n",
+    "def init_csv(dataset_path: str):\n",
+    "    '''\n",
+    "    Create a blank csv file with just columns\n",
+    "    '''\n",
+    "\n",
+    "    # Ignore if file is already exist\n",
+    "    if os.path.exists(dataset_path):\n",
+    "        return\n",
+    "\n",
+    "    # Write all the columns to a empty file\n",
+    "    with open(dataset_path, mode=\"w\", newline=\"\") as f:\n",
+    "        csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "        csv_writer.writerow(HEADERS)\n",
+    "\n",
+    "\n",
+    "def export_landmark_to_csv(dataset_path: str, results, action: str) -> None:\n",
+    "    '''\n",
+    "    Export Labeled Data from detected landmark to csv\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "    keypoints = []\n",
+    "\n",
+    "    try:\n",
+    "        # Extract coordinate of important landmarks\n",
+    "        for lm in IMPORTANT_LMS:\n",
+    "            keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "            keypoints.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "        \n",
+    "        keypoints = list(np.array(keypoints).flatten())\n",
+    "\n",
+    "        # Insert action as the label (first column)\n",
+    "        keypoints.insert(0, action)\n",
+    "\n",
+    "        # Append new row to .csv file\n",
+    "        with open(dataset_path, mode=\"a\", newline=\"\") as f:\n",
+    "            csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "            csv_writer.writerow(keypoints)\n",
+    "        \n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_dataset.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "    \n",
+    "\n",
+    "def concat_csv_files_with_same_headers(file_paths: list, saved_path: str):\n",
+    "    '''\n",
+    "    Concat different csv files\n",
+    "    '''\n",
+    "    all_df = []\n",
+    "    for path in file_paths:\n",
+    "        df = pd.read_csv(path, index_col=None, header=0)\n",
+    "        all_df.append(df)\n",
+    "    \n",
+    "    results = pd.concat(all_df, axis=0, ignore_index=True)\n",
+    "    results.to_csv(saved_path, sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
+    "    '''\n",
+    "    Calculate the angle between 3 points\n",
+    "    Unit of the angle will be in Degree\n",
+    "    '''\n",
+    "    point1 = np.array(point1)\n",
+    "    point2 = np.array(point2)\n",
+    "    point3 = np.array(point3)\n",
+    "\n",
+    "    # Calculate algo\n",
+    "    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
+    "    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
+    "\n",
+    "    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
+    "    return angleInDeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Extract data from video for train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_PATH = \"stage.train.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/lunge/lunge_1.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "# init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.8) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "        \n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "        # Calculate right knee angle\n",
+    "        right_hip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]\n",
+    "        right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
+    "        right_ankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]\n",
+    "        right_knee_angle = calculate_angle(right_hip, right_knee, right_ankle)\n",
+    "\n",
+    "        # Calculate left knee angle\n",
+    "        left_hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]\n",
+    "        left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
+    "        left_ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]\n",
+    "        left_knee_angle = calculate_angle(left_hip, left_knee, left_ankle)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        # Visualize angles\n",
+    "        cv2.putText(image, str(int(right_knee_angle)), tuple(np.multiply(right_knee, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "        cv2.putText(image, str(int(left_knee_angle)), tuple(np.multiply(left_knee, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # * Press I to save as INIT stage\n",
+    "        if k == ord('i'): \n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"I\")\n",
+    "            save_counts += 1\n",
+    "        # * Press M to save as MID stage\n",
+    "        elif k == ord(\"m\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"M\")\n",
+    "            save_counts += 1\n",
+    "        # * Press D to save as DOWN stage\n",
+    "        elif k == ord(\"d\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"D\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 24244 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "D    8232\n",
+      "M    8148\n",
+      "I    7864\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 151\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>nose_x</th>\n",
+       "      <th>nose_y</th>\n",
+       "      <th>nose_z</th>\n",
+       "      <th>nose_v</th>\n",
+       "      <th>left_shoulder_x</th>\n",
+       "      <th>left_shoulder_y</th>\n",
+       "      <th>left_shoulder_z</th>\n",
+       "      <th>left_shoulder_v</th>\n",
+       "      <th>right_shoulder_x</th>\n",
+       "      <th>...</th>\n",
+       "      <th>right_heel_z</th>\n",
+       "      <th>right_heel_v</th>\n",
+       "      <th>left_foot_index_x</th>\n",
+       "      <th>left_foot_index_y</th>\n",
+       "      <th>left_foot_index_z</th>\n",
+       "      <th>left_foot_index_v</th>\n",
+       "      <th>right_foot_index_x</th>\n",
+       "      <th>right_foot_index_y</th>\n",
+       "      <th>right_foot_index_z</th>\n",
+       "      <th>right_foot_index_v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24241</th>\n",
+       "      <td>I</td>\n",
+       "      <td>0.594462</td>\n",
+       "      <td>0.076696</td>\n",
+       "      <td>-0.136184</td>\n",
+       "      <td>0.999170</td>\n",
+       "      <td>0.569238</td>\n",
+       "      <td>0.148132</td>\n",
+       "      <td>0.136019</td>\n",
+       "      <td>0.996545</td>\n",
+       "      <td>0.538435</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.059251</td>\n",
+       "      <td>0.901369</td>\n",
+       "      <td>0.683448</td>\n",
+       "      <td>0.719196</td>\n",
+       "      <td>0.156960</td>\n",
+       "      <td>0.703809</td>\n",
+       "      <td>0.607568</td>\n",
+       "      <td>0.868497</td>\n",
+       "      <td>-0.168608</td>\n",
+       "      <td>0.939461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24242</th>\n",
+       "      <td>I</td>\n",
+       "      <td>0.596996</td>\n",
+       "      <td>0.078281</td>\n",
+       "      <td>-0.125908</td>\n",
+       "      <td>0.998940</td>\n",
+       "      <td>0.571820</td>\n",
+       "      <td>0.148404</td>\n",
+       "      <td>0.142542</td>\n",
+       "      <td>0.996117</td>\n",
+       "      <td>0.544044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.034277</td>\n",
+       "      <td>0.893682</td>\n",
+       "      <td>0.661107</td>\n",
+       "      <td>0.704423</td>\n",
+       "      <td>0.169164</td>\n",
+       "      <td>0.679100</td>\n",
+       "      <td>0.610006</td>\n",
+       "      <td>0.863795</td>\n",
+       "      <td>-0.139861</td>\n",
+       "      <td>0.934157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24243</th>\n",
+       "      <td>I</td>\n",
+       "      <td>0.603530</td>\n",
+       "      <td>0.082330</td>\n",
+       "      <td>-0.120693</td>\n",
+       "      <td>0.998889</td>\n",
+       "      <td>0.573687</td>\n",
+       "      <td>0.147178</td>\n",
+       "      <td>0.117238</td>\n",
+       "      <td>0.995724</td>\n",
+       "      <td>0.550559</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.034098</td>\n",
+       "      <td>0.904877</td>\n",
+       "      <td>0.690529</td>\n",
+       "      <td>0.595529</td>\n",
+       "      <td>0.226050</td>\n",
+       "      <td>0.676888</td>\n",
+       "      <td>0.625108</td>\n",
+       "      <td>0.845298</td>\n",
+       "      <td>-0.132410</td>\n",
+       "      <td>0.941881</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      label    nose_x    nose_y    nose_z    nose_v  left_shoulder_x  \\\n",
+       "24241     I  0.594462  0.076696 -0.136184  0.999170         0.569238   \n",
+       "24242     I  0.596996  0.078281 -0.125908  0.998940         0.571820   \n",
+       "24243     I  0.603530  0.082330 -0.120693  0.998889         0.573687   \n",
+       "\n",
+       "       left_shoulder_y  left_shoulder_z  left_shoulder_v  right_shoulder_x  \\\n",
+       "24241         0.148132         0.136019         0.996545          0.538435   \n",
+       "24242         0.148404         0.142542         0.996117          0.544044   \n",
+       "24243         0.147178         0.117238         0.995724          0.550559   \n",
+       "\n",
+       "       ...  right_heel_z  right_heel_v  left_foot_index_x  left_foot_index_y  \\\n",
+       "24241  ...     -0.059251      0.901369           0.683448           0.719196   \n",
+       "24242  ...     -0.034277      0.893682           0.661107           0.704423   \n",
+       "24243  ...     -0.034098      0.904877           0.690529           0.595529   \n",
+       "\n",
+       "       left_foot_index_z  left_foot_index_v  right_foot_index_x  \\\n",
+       "24241           0.156960           0.703809            0.607568   \n",
+       "24242           0.169164           0.679100            0.610006   \n",
+       "24243           0.226050           0.676888            0.625108   \n",
+       "\n",
+       "       right_foot_index_y  right_foot_index_z  right_foot_index_v  \n",
+       "24241            0.868497           -0.168608            0.939461  \n",
+       "24242            0.863795           -0.139861            0.934157  \n",
+       "24243            0.845298           -0.132410            0.941881  \n",
+       "\n",
+       "[3 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(DATASET_PATH)\n",
+    "df.tail(3)\n",
+    "\n",
+    "# remove_duplicate_rows(DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Extract data for testset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "TEST_DATASET_PATH = \"stage.test.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/lunge/lunge_test_3.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "init_csv(TEST_DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.8) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "        \n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        wait_time = 300\n",
+    "        k = cv2.waitKey(wait_time) & 0xFF\n",
+    "\n",
+    "        # * Press I to save as INIT stage\n",
+    "        if k == ord('i'): \n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"I\")\n",
+    "            save_counts += 1\n",
+    "        # * Press M to save as MID stage\n",
+    "        elif k == ord(\"m\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"M\")\n",
+    "            save_counts += 1\n",
+    "        # * Press D to save as DOWN stage\n",
+    "        elif k == ord(\"d\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"D\")\n",
+    "            save_counts += 1\n",
+    "        # * Press A to change wait time\n",
+    "        elif k == ord(\"a\"):\n",
+    "            time.sleep(4)\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 1205 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "D    416\n",
+      "I    402\n",
+      "M    387\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test_df = pd.read_csv(TEST_DATASET_PATH)\n",
+    "# test_df.drop(['right_knee_angle', 'left_knee_angle'], axis=1, inplace=True)\n",
+    "# test_df.to_csv(TEST_DATASET_PATH, sep=',', encoding='utf-8', index=False)\n",
+    "test_df = describe_dataset(TEST_DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Class Balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='count', ylabel='label'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(y='label', data=df, palette=\"Set1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Process Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>angle</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>82.006673</td>\n",
+       "      <td>left_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>74.160646</td>\n",
+       "      <td>left_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>73.564828</td>\n",
+       "      <td>left_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>73.541237</td>\n",
+       "      <td>left_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>71.950243</td>\n",
+       "      <td>left_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9901</th>\n",
+       "      <td>84.635022</td>\n",
+       "      <td>right_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9902</th>\n",
+       "      <td>88.030435</td>\n",
+       "      <td>right_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9903</th>\n",
+       "      <td>91.017440</td>\n",
+       "      <td>right_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9904</th>\n",
+       "      <td>96.022529</td>\n",
+       "      <td>right_knee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9905</th>\n",
+       "      <td>97.528293</td>\n",
+       "      <td>right_knee</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9906 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          angle       label\n",
+       "0     82.006673   left_knee\n",
+       "1     74.160646   left_knee\n",
+       "2     73.564828   left_knee\n",
+       "3     73.541237   left_knee\n",
+       "4     71.950243   left_knee\n",
+       "...         ...         ...\n",
+       "9901  84.635022  right_knee\n",
+       "9902  88.030435  right_knee\n",
+       "9903  91.017440  right_knee\n",
+       "9904  96.022529  right_knee\n",
+       "9905  97.528293  right_knee\n",
+       "\n",
+       "[9906 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knee_angle_df = pd.read_csv(\"./knee_angle_2.csv\")\n",
+    "knee_angle_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='angle', ylabel='label'>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"angle\", y=\"label\", data=knee_angle_df, palette=\"viridis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Conclusion**\n",
+    "\n",
+    "As seen from the 2 graph, the majority of knee angle fall between **60 <= angle <= 135**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/2.stage.sklearn.ipynb b/core/lunge_model/2.stage.sklearn.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2127d30aa870f9c494ba6a188e5979b36293a92f
--- /dev/null
+++ b/core/lunge_model/2.stage.sklearn.ipynb
@@ -0,0 +1,753 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[21024]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x105f48860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15aece480). One of the two will be used. Which one is undefined.\n",
+      "objc[21024]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x104310a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15aece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[21024]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x104310a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15aece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[21024]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x104310ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15aece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Train Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Describe data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 24244 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "D    8232\n",
+      "M    8148\n",
+      "I    7864\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 151\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>nose_x</th>\n",
+       "      <th>nose_y</th>\n",
+       "      <th>nose_z</th>\n",
+       "      <th>nose_v</th>\n",
+       "      <th>left_shoulder_x</th>\n",
+       "      <th>left_shoulder_y</th>\n",
+       "      <th>left_shoulder_z</th>\n",
+       "      <th>left_shoulder_v</th>\n",
+       "      <th>right_shoulder_x</th>\n",
+       "      <th>...</th>\n",
+       "      <th>right_heel_z</th>\n",
+       "      <th>right_heel_v</th>\n",
+       "      <th>left_foot_index_x</th>\n",
+       "      <th>left_foot_index_y</th>\n",
+       "      <th>left_foot_index_z</th>\n",
+       "      <th>left_foot_index_v</th>\n",
+       "      <th>right_foot_index_x</th>\n",
+       "      <th>right_foot_index_y</th>\n",
+       "      <th>right_foot_index_z</th>\n",
+       "      <th>right_foot_index_v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>M</td>\n",
+       "      <td>0.496085</td>\n",
+       "      <td>0.286904</td>\n",
+       "      <td>-0.219098</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>0.500287</td>\n",
+       "      <td>0.360987</td>\n",
+       "      <td>0.019479</td>\n",
+       "      <td>0.999978</td>\n",
+       "      <td>0.436462</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.268695</td>\n",
+       "      <td>0.996758</td>\n",
+       "      <td>0.370391</td>\n",
+       "      <td>0.893386</td>\n",
+       "      <td>0.505172</td>\n",
+       "      <td>0.931761</td>\n",
+       "      <td>0.566927</td>\n",
+       "      <td>1.005949</td>\n",
+       "      <td>-0.382462</td>\n",
+       "      <td>0.998906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>M</td>\n",
+       "      <td>0.496126</td>\n",
+       "      <td>0.286918</td>\n",
+       "      <td>-0.217849</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>0.500281</td>\n",
+       "      <td>0.360954</td>\n",
+       "      <td>0.019995</td>\n",
+       "      <td>0.999977</td>\n",
+       "      <td>0.436466</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.271191</td>\n",
+       "      <td>0.996724</td>\n",
+       "      <td>0.370344</td>\n",
+       "      <td>0.893290</td>\n",
+       "      <td>0.505325</td>\n",
+       "      <td>0.931969</td>\n",
+       "      <td>0.567040</td>\n",
+       "      <td>1.005795</td>\n",
+       "      <td>-0.384848</td>\n",
+       "      <td>0.998902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>M</td>\n",
+       "      <td>0.496144</td>\n",
+       "      <td>0.286921</td>\n",
+       "      <td>-0.217039</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>0.500279</td>\n",
+       "      <td>0.360923</td>\n",
+       "      <td>0.020068</td>\n",
+       "      <td>0.999977</td>\n",
+       "      <td>0.436469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.271365</td>\n",
+       "      <td>0.996699</td>\n",
+       "      <td>0.370316</td>\n",
+       "      <td>0.893275</td>\n",
+       "      <td>0.504931</td>\n",
+       "      <td>0.931633</td>\n",
+       "      <td>0.567040</td>\n",
+       "      <td>1.005774</td>\n",
+       "      <td>-0.384872</td>\n",
+       "      <td>0.998894</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label    nose_x    nose_y    nose_z    nose_v  left_shoulder_x  \\\n",
+       "0     M  0.496085  0.286904 -0.219098  0.999996         0.500287   \n",
+       "1     M  0.496126  0.286918 -0.217849  0.999996         0.500281   \n",
+       "2     M  0.496144  0.286921 -0.217039  0.999996         0.500279   \n",
+       "\n",
+       "   left_shoulder_y  left_shoulder_z  left_shoulder_v  right_shoulder_x  ...  \\\n",
+       "0         0.360987         0.019479         0.999978          0.436462  ...   \n",
+       "1         0.360954         0.019995         0.999977          0.436466  ...   \n",
+       "2         0.360923         0.020068         0.999977          0.436469  ...   \n",
+       "\n",
+       "   right_heel_z  right_heel_v  left_foot_index_x  left_foot_index_y  \\\n",
+       "0     -0.268695      0.996758           0.370391           0.893386   \n",
+       "1     -0.271191      0.996724           0.370344           0.893290   \n",
+       "2     -0.271365      0.996699           0.370316           0.893275   \n",
+       "\n",
+       "   left_foot_index_z  left_foot_index_v  right_foot_index_x  \\\n",
+       "0           0.505172           0.931761            0.566927   \n",
+       "1           0.505325           0.931969            0.567040   \n",
+       "2           0.504931           0.931633            0.567040   \n",
+       "\n",
+       "   right_foot_index_y  right_foot_index_z  right_foot_index_v  \n",
+       "0            1.005949           -0.382462            0.998906  \n",
+       "1            1.005795           -0.384848            0.998902  \n",
+       "2            1.005774           -0.384872            0.998894  \n",
+       "\n",
+       "[3 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(\"./stage.train.csv\")\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Train and evaluate model with train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression, RidgeClassifier, SGDClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract features and class\n",
+    "X = df.drop(\"label\", axis=1) # features\n",
+    "y = df[\"label\"]\n",
+    "\n",
+    "# Standard Scaler\n",
+    "sc = StandardScaler()\n",
+    "X = pd.DataFrame(sc.fit_transform(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8474    M\n",
+       "4197    I\n",
+       "9705    I\n",
+       "Name: label, dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
+    "\n",
+    "y_train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "algorithms =[(\"LR\", LogisticRegression()),\n",
+    "         (\"SVC\", SVC(probability=True)),\n",
+    "         ('KNN',KNeighborsClassifier()),\n",
+    "         (\"DTC\", DecisionTreeClassifier()),\n",
+    "         (\"SGDC\", SGDClassifier()),\n",
+    "         (\"Ridge\", RidgeClassifier()),\n",
+    "         ('RF', RandomForestClassifier()),]\n",
+    "\n",
+    "models = {}\n",
+    "final_results = []\n",
+    "\n",
+    "for name, model in algorithms:\n",
+    "    trained_model = model.fit(X_train, y_train)\n",
+    "    models[name] = trained_model\n",
+    "\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(X_test)\n",
+    "\n",
+    "    p_score = precision_score(y_test, model_results, average=\"macro\")\n",
+    "    a_score = accuracy_score(y_test, model_results)\n",
+    "    r_score = recall_score(y_test, model_results, average=\"macro\")\n",
+    "    f1_score_result = f1_score(y_test, model_results, average=None, labels=[\"I\", \"M\", \"D\"])\n",
+    "    final_results.append(( name, p_score, a_score, r_score, f1_score_result ))\n",
+    "\n",
+    "\n",
+    "final_results.sort(key=lambda k: k[4][0] + k[4][1], reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.995486</td>\n",
+       "      <td>0.995463</td>\n",
+       "      <td>0.995497</td>\n",
+       "      <td>[0.998108448928121, 0.9936189608021876, 0.9947...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.992812</td>\n",
+       "      <td>0.992782</td>\n",
+       "      <td>0.992862</td>\n",
+       "      <td>[0.9977952755905511, 0.9893390191897654, 0.991...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.993596</td>\n",
+       "      <td>0.993607</td>\n",
+       "      <td>0.993679</td>\n",
+       "      <td>[0.9949717159019483, 0.9905516610789394, 0.995...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.989931</td>\n",
+       "      <td>0.989895</td>\n",
+       "      <td>0.990009</td>\n",
+       "      <td>[0.9959080893925086, 0.9850381679389313, 0.988...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.990106</td>\n",
+       "      <td>0.990101</td>\n",
+       "      <td>0.990208</td>\n",
+       "      <td>[0.9943431803896919, 0.9856576136710405, 0.990...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.986400</td>\n",
+       "      <td>0.986389</td>\n",
+       "      <td>0.986541</td>\n",
+       "      <td>[0.990894819466248, 0.9797794117647058, 0.9886...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ridge</td>\n",
+       "      <td>0.970926</td>\n",
+       "      <td>0.970097</td>\n",
+       "      <td>0.969980</td>\n",
+       "      <td>[0.9709677419354839, 0.9567827130852341, 0.982...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Model  Precision Score  Accuracy score  Recall Score  \\\n",
+       "0    KNN         0.995486        0.995463      0.995497   \n",
+       "1    SVC         0.992812        0.992782      0.992862   \n",
+       "2     RF         0.993596        0.993607      0.993679   \n",
+       "3     LR         0.989931        0.989895      0.990009   \n",
+       "4    DTC         0.990106        0.990101      0.990208   \n",
+       "5   SGDC         0.986400        0.986389      0.986541   \n",
+       "6  Ridge         0.970926        0.970097      0.969980   \n",
+       "\n",
+       "                                            F1 score  \n",
+       "0  [0.998108448928121, 0.9936189608021876, 0.9947...  \n",
+       "1  [0.9977952755905511, 0.9893390191897654, 0.991...  \n",
+       "2  [0.9949717159019483, 0.9905516610789394, 0.995...  \n",
+       "3  [0.9959080893925086, 0.9850381679389313, 0.988...  \n",
+       "4  [0.9943431803896919, 0.9856576136710405, 0.990...  \n",
+       "5  [0.990894819466248, 0.9797794117647058, 0.9886...  \n",
+       "6  [0.9709677419354839, 0.9567827130852341, 0.982...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Evaluate models with test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 1205 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "D    416\n",
+      "I    402\n",
+      "M    387\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(\"./stage.test.csv\")\n",
+    "test_df = test_df.sample(frac=1).reset_index(drop=True)\n",
+    "\n",
+    "test_x = test_df.drop(\"label\", axis=1)\n",
+    "test_y = test_df[\"label\"]\n",
+    "\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ridge</td>\n",
+       "      <td>0.953408</td>\n",
+       "      <td>0.951037</td>\n",
+       "      <td>0.949563</td>\n",
+       "      <td>[0.9763387297633873, 0.9199457259158751, 0.954...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.955640</td>\n",
+       "      <td>0.951867</td>\n",
+       "      <td>0.950163</td>\n",
+       "      <td>[0.9492325855962219, 0.9194444444444445, 0.982...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.952856</td>\n",
+       "      <td>0.948548</td>\n",
+       "      <td>0.946658</td>\n",
+       "      <td>[0.950354609929078, 0.9131652661064426, 0.9764...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.919799</td>\n",
+       "      <td>0.915353</td>\n",
+       "      <td>0.916588</td>\n",
+       "      <td>[0.9745454545454546, 0.875609756097561, 0.8941...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.928256</td>\n",
+       "      <td>0.912863</td>\n",
+       "      <td>0.909741</td>\n",
+       "      <td>[0.8903654485049834, 0.8444444444444444, 0.992...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.870546</td>\n",
+       "      <td>0.862241</td>\n",
+       "      <td>0.861943</td>\n",
+       "      <td>[0.90744920993228, 0.7849740932642487, 0.88829...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.859608</td>\n",
+       "      <td>0.857261</td>\n",
+       "      <td>0.855094</td>\n",
+       "      <td>[0.8983240223463688, 0.7577464788732395, 0.899...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Model  Precision Score  Accuracy score  Recall Score  \\\n",
+       "0  Ridge         0.953408        0.951037      0.949563   \n",
+       "1    SVC         0.955640        0.951867      0.950163   \n",
+       "2     LR         0.952856        0.948548      0.946658   \n",
+       "3    KNN         0.919799        0.915353      0.916588   \n",
+       "4   SGDC         0.928256        0.912863      0.909741   \n",
+       "5     RF         0.870546        0.862241      0.861943   \n",
+       "6    DTC         0.859608        0.857261      0.855094   \n",
+       "\n",
+       "                                            F1 score  \n",
+       "0  [0.9763387297633873, 0.9199457259158751, 0.954...  \n",
+       "1  [0.9492325855962219, 0.9194444444444445, 0.982...  \n",
+       "2  [0.950354609929078, 0.9131652661064426, 0.9764...  \n",
+       "3  [0.9745454545454546, 0.875609756097561, 0.8941...  \n",
+       "4  [0.8903654485049834, 0.8444444444444444, 0.992...  \n",
+       "5  [0.90744920993228, 0.7849740932642487, 0.88829...  \n",
+       "6  [0.8983240223463688, 0.7577464788732395, 0.899...  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=\"macro\")\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=\"macro\")\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=None, labels=[\"I\", \"M\", \"D\"])\n",
+    "    testset_final_results.append(( name, p_score, a_score, r_score, f1_score_result ))\n",
+    "\n",
+    "\n",
+    "testset_final_results.sort(key=lambda k: k[4][0] + k[4][1], reverse=True)\n",
+    "pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Dumped Model\n",
+    "\n",
+    "The best models are in order:\n",
+    "- Ridge\n",
+    "- SVC\n",
+    "- LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/stage_LR_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"LR\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/stage_SVC_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"SVC\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/stage_Ridge_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"Ridge\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(sc, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/3.stage.deep_learning.ipynb b/core/lunge_model/3.stage.deep_learning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bd65035f0bd3cac25aebb56eb8bc3d69dbfaf08f
--- /dev/null
+++ b/core/lunge_model/3.stage.deep_learning.ipynb
@@ -0,0 +1,767 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data visualization\n",
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "# Keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "# Train-Test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# Classification Report\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Describe dataset and Train Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Describe dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 17040 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "M    6171\n",
+      "D    5735\n",
+      "I    5134\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "# Remove duplicate rows (optional)\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_dataset.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "df = describe_dataset(\"./dataset.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows: 17040 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "1    6171\n",
+      "2    5735\n",
+      "0    5134\n",
+      "Name: label, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "df = pd.read_csv(\"./dataset.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"I\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"M\", \"label\"] = 1\n",
+    "df.loc[df[\"label\"] == \"D\", \"label\"] = 2\n",
+    "\n",
+    "print(f'Number of rows: {df.shape[0]} \\nNumber of columns: {df.shape[1]}\\n')\n",
+    "print(f\"Labels: \\n{df['label'].value_counts()}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f2:\n",
+    "    input_scaler = pickle.load(f2)\n",
+    "\n",
+    "x = df.drop(\"label\", axis = 1)\n",
+    "x = pd.DataFrame(input_scaler.transform(x))\n",
+    "\n",
+    "y = df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n",
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense (Dense)               (None, 52)                2756      \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 52)                0         \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 52)                2756      \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 52)                0         \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 14)                742       \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 3)                 45        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 6,299\n",
+      "Trainable params: 6,299\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-14 10:38:36.786157: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2022-11-14 10:38:36.786920: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(52, input_dim = 52, activation = \"relu\"))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(52, activation = \"relu\"))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(14, activation = \"relu\"))\n",
+    "model.add(Dense(3, activation = \"softmax\"))\n",
+    "model.compile(Adam(lr = 0.01), \"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-14 10:38:48.571461: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
+      "2022-11-14 10:38:48.827690: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1364/1364 [==============================] - ETA: 0s - loss: 0.1082 - accuracy: 0.9696"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-14 10:39:02.176190: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.1082 - accuracy: 0.9696 - val_loss: 0.0339 - val_accuracy: 0.9947\n",
+      "Epoch 2/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0599 - accuracy: 0.9872 - val_loss: 0.0595 - val_accuracy: 0.9815\n",
+      "Epoch 3/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0527 - accuracy: 0.9885 - val_loss: 0.0530 - val_accuracy: 0.9944\n",
+      "Epoch 4/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0450 - accuracy: 0.9905 - val_loss: 0.0356 - val_accuracy: 0.9938\n",
+      "Epoch 5/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.1253 - accuracy: 0.9747 - val_loss: 0.0501 - val_accuracy: 0.9921\n",
+      "Epoch 6/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0682 - accuracy: 0.9884 - val_loss: 0.0445 - val_accuracy: 0.9865\n",
+      "Epoch 7/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0534 - accuracy: 0.9900 - val_loss: 0.0400 - val_accuracy: 0.9933\n",
+      "Epoch 8/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0627 - accuracy: 0.9913 - val_loss: 0.0396 - val_accuracy: 0.9950\n",
+      "Epoch 9/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0556 - accuracy: 0.9905 - val_loss: 0.0397 - val_accuracy: 0.9953\n",
+      "Epoch 10/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0656 - accuracy: 0.9900 - val_loss: 0.0657 - val_accuracy: 0.9795\n",
+      "Epoch 11/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0661 - accuracy: 0.9862 - val_loss: 0.0454 - val_accuracy: 0.9933\n",
+      "Epoch 12/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0638 - accuracy: 0.9894 - val_loss: 0.0404 - val_accuracy: 0.9930\n",
+      "Epoch 13/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0643 - accuracy: 0.9911 - val_loss: 0.1060 - val_accuracy: 0.9668\n",
+      "Epoch 14/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1065 - accuracy: 0.9883 - val_loss: 0.0645 - val_accuracy: 0.9844\n",
+      "Epoch 15/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0914 - accuracy: 0.9858 - val_loss: 0.0930 - val_accuracy: 0.9683\n",
+      "Epoch 16/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0463 - accuracy: 0.9916 - val_loss: 0.0557 - val_accuracy: 0.9886\n",
+      "Epoch 17/100\n",
+      "1364/1364 [==============================] - 14s 11ms/step - loss: 0.1247 - accuracy: 0.9880 - val_loss: 0.0620 - val_accuracy: 0.9886\n",
+      "Epoch 18/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1441 - accuracy: 0.9807 - val_loss: 0.0483 - val_accuracy: 0.9935\n",
+      "Epoch 19/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.1044 - accuracy: 0.9886 - val_loss: 0.0375 - val_accuracy: 0.9944\n",
+      "Epoch 20/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0604 - accuracy: 0.9900 - val_loss: 0.0382 - val_accuracy: 0.9933\n",
+      "Epoch 21/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0758 - accuracy: 0.9916 - val_loss: 0.0461 - val_accuracy: 0.9933\n",
+      "Epoch 22/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0413 - accuracy: 0.9916 - val_loss: 0.1001 - val_accuracy: 0.9704\n",
+      "Epoch 23/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0492 - accuracy: 0.9934 - val_loss: 0.0482 - val_accuracy: 0.9959\n",
+      "Epoch 24/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0689 - accuracy: 0.9897 - val_loss: 0.0374 - val_accuracy: 0.9950\n",
+      "Epoch 25/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0663 - accuracy: 0.9911 - val_loss: 0.0341 - val_accuracy: 0.9944\n",
+      "Epoch 26/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0983 - accuracy: 0.9904 - val_loss: 0.0470 - val_accuracy: 0.9933\n",
+      "Epoch 27/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0699 - accuracy: 0.9906 - val_loss: 0.0455 - val_accuracy: 0.9921\n",
+      "Epoch 28/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0521 - accuracy: 0.9908 - val_loss: 0.0438 - val_accuracy: 0.9915\n",
+      "Epoch 29/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0594 - accuracy: 0.9904 - val_loss: 0.0490 - val_accuracy: 0.9930\n",
+      "Epoch 30/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.1044 - accuracy: 0.9903 - val_loss: 0.0439 - val_accuracy: 0.9933\n",
+      "Epoch 31/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0414 - accuracy: 0.9930 - val_loss: 0.0664 - val_accuracy: 0.9806\n",
+      "Epoch 32/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0507 - accuracy: 0.9927 - val_loss: 0.1468 - val_accuracy: 0.9563\n",
+      "Epoch 33/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0745 - accuracy: 0.9907 - val_loss: 0.0730 - val_accuracy: 0.9742\n",
+      "Epoch 34/100\n",
+      "1364/1364 [==============================] - 14s 11ms/step - loss: 0.0595 - accuracy: 0.9896 - val_loss: 0.0525 - val_accuracy: 0.9933\n",
+      "Epoch 35/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0849 - accuracy: 0.9884 - val_loss: 0.0396 - val_accuracy: 0.9941\n",
+      "Epoch 36/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0738 - accuracy: 0.9882 - val_loss: 0.0384 - val_accuracy: 0.9950\n",
+      "Epoch 37/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1039 - accuracy: 0.9892 - val_loss: 0.0417 - val_accuracy: 0.9941\n",
+      "Epoch 38/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1345 - accuracy: 0.9883 - val_loss: 0.0380 - val_accuracy: 0.9921\n",
+      "Epoch 39/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0483 - accuracy: 0.9930 - val_loss: 0.0325 - val_accuracy: 0.9959\n",
+      "Epoch 40/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0427 - accuracy: 0.9931 - val_loss: 0.0399 - val_accuracy: 0.9947\n",
+      "Epoch 41/100\n",
+      "1364/1364 [==============================] - 14s 11ms/step - loss: 0.0430 - accuracy: 0.9924 - val_loss: 0.0555 - val_accuracy: 0.9950\n",
+      "Epoch 42/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0536 - accuracy: 0.9917 - val_loss: 0.0533 - val_accuracy: 0.9933\n",
+      "Epoch 43/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.1049 - accuracy: 0.9912 - val_loss: 0.0374 - val_accuracy: 0.9935\n",
+      "Epoch 44/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0831 - accuracy: 0.9930 - val_loss: 0.0339 - val_accuracy: 0.9950\n",
+      "Epoch 45/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0529 - accuracy: 0.9920 - val_loss: 0.0365 - val_accuracy: 0.9953\n",
+      "Epoch 46/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0634 - accuracy: 0.9909 - val_loss: 0.0412 - val_accuracy: 0.9947\n",
+      "Epoch 47/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0626 - accuracy: 0.9917 - val_loss: 0.0390 - val_accuracy: 0.9956\n",
+      "Epoch 48/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1139 - accuracy: 0.9907 - val_loss: 0.0426 - val_accuracy: 0.9944\n",
+      "Epoch 49/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0622 - accuracy: 0.9902 - val_loss: 0.0400 - val_accuracy: 0.9938\n",
+      "Epoch 50/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0857 - accuracy: 0.9900 - val_loss: 0.0980 - val_accuracy: 0.9862\n",
+      "Epoch 51/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1315 - accuracy: 0.9900 - val_loss: 0.0488 - val_accuracy: 0.9944\n",
+      "Epoch 52/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0508 - accuracy: 0.9925 - val_loss: 0.0433 - val_accuracy: 0.9944\n",
+      "Epoch 53/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0558 - accuracy: 0.9919 - val_loss: 0.0418 - val_accuracy: 0.9938\n",
+      "Epoch 54/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0391 - accuracy: 0.9937 - val_loss: 0.0398 - val_accuracy: 0.9947\n",
+      "Epoch 55/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0447 - accuracy: 0.9938 - val_loss: 0.0511 - val_accuracy: 0.9953\n",
+      "Epoch 56/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0377 - accuracy: 0.9937 - val_loss: 0.0454 - val_accuracy: 0.9933\n",
+      "Epoch 57/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0467 - accuracy: 0.9927 - val_loss: 0.0542 - val_accuracy: 0.9912\n",
+      "Epoch 58/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0450 - accuracy: 0.9925 - val_loss: 0.0441 - val_accuracy: 0.9938\n",
+      "Epoch 59/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0541 - accuracy: 0.9938 - val_loss: 0.0586 - val_accuracy: 0.9839\n",
+      "Epoch 60/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0363 - accuracy: 0.9936 - val_loss: 0.0418 - val_accuracy: 0.9944\n",
+      "Epoch 61/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0612 - accuracy: 0.9919 - val_loss: 0.0478 - val_accuracy: 0.9918\n",
+      "Epoch 62/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0545 - accuracy: 0.9922 - val_loss: 0.0776 - val_accuracy: 0.9850\n",
+      "Epoch 63/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0663 - accuracy: 0.9910 - val_loss: 0.0660 - val_accuracy: 0.9933\n",
+      "Epoch 64/100\n",
+      "1364/1364 [==============================] - 16s 11ms/step - loss: 0.0597 - accuracy: 0.9920 - val_loss: 0.0792 - val_accuracy: 0.9850\n",
+      "Epoch 65/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0395 - accuracy: 0.9927 - val_loss: 0.1932 - val_accuracy: 0.9935\n",
+      "Epoch 66/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1077 - accuracy: 0.9911 - val_loss: 0.0754 - val_accuracy: 0.9806\n",
+      "Epoch 67/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0505 - accuracy: 0.9917 - val_loss: 0.1537 - val_accuracy: 0.9824\n",
+      "Epoch 68/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1343 - accuracy: 0.9897 - val_loss: 0.0956 - val_accuracy: 0.9921\n",
+      "Epoch 69/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0766 - accuracy: 0.9871 - val_loss: 0.0841 - val_accuracy: 0.9880\n",
+      "Epoch 70/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.1537 - accuracy: 0.9734 - val_loss: 0.0734 - val_accuracy: 0.9933\n",
+      "Epoch 71/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0720 - accuracy: 0.9884 - val_loss: 0.0573 - val_accuracy: 0.9935\n",
+      "Epoch 72/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0545 - accuracy: 0.9928 - val_loss: 0.0505 - val_accuracy: 0.9927\n",
+      "Epoch 73/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0748 - accuracy: 0.9923 - val_loss: 0.0453 - val_accuracy: 0.9950\n",
+      "Epoch 74/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0661 - accuracy: 0.9891 - val_loss: 0.0652 - val_accuracy: 0.9941\n",
+      "Epoch 75/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0676 - accuracy: 0.9893 - val_loss: 0.0467 - val_accuracy: 0.9935\n",
+      "Epoch 76/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0601 - accuracy: 0.9891 - val_loss: 0.1189 - val_accuracy: 0.9944\n",
+      "Epoch 77/100\n",
+      "1364/1364 [==============================] - 14s 11ms/step - loss: 0.0404 - accuracy: 0.9940 - val_loss: 0.0557 - val_accuracy: 0.9935\n",
+      "Epoch 78/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0686 - accuracy: 0.9932 - val_loss: 0.0495 - val_accuracy: 0.9941\n",
+      "Epoch 79/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0409 - accuracy: 0.9943 - val_loss: 0.0618 - val_accuracy: 0.9950\n",
+      "Epoch 80/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0613 - accuracy: 0.9928 - val_loss: 0.0438 - val_accuracy: 0.9944\n",
+      "Epoch 81/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0818 - accuracy: 0.9893 - val_loss: 0.0639 - val_accuracy: 0.9935\n",
+      "Epoch 82/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0558 - accuracy: 0.9898 - val_loss: 0.0750 - val_accuracy: 0.9933\n",
+      "Epoch 83/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0568 - accuracy: 0.9919 - val_loss: 0.0522 - val_accuracy: 0.9950\n",
+      "Epoch 84/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0707 - accuracy: 0.9927 - val_loss: 0.0336 - val_accuracy: 0.9950\n",
+      "Epoch 85/100\n",
+      "1364/1364 [==============================] - 15s 11ms/step - loss: 0.0615 - accuracy: 0.9929 - val_loss: 0.0433 - val_accuracy: 0.9953\n",
+      "Epoch 86/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0375 - accuracy: 0.9943 - val_loss: 0.1049 - val_accuracy: 0.9947\n",
+      "Epoch 87/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0372 - accuracy: 0.9942 - val_loss: 0.0892 - val_accuracy: 0.9950\n",
+      "Epoch 88/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0641 - accuracy: 0.9927 - val_loss: 0.0985 - val_accuracy: 0.9935\n",
+      "Epoch 89/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0512 - accuracy: 0.9913 - val_loss: 0.0580 - val_accuracy: 0.9950\n",
+      "Epoch 90/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0864 - accuracy: 0.9921 - val_loss: 0.1047 - val_accuracy: 0.9950\n",
+      "Epoch 91/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0407 - accuracy: 0.9934 - val_loss: 0.0739 - val_accuracy: 0.9944\n",
+      "Epoch 92/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0419 - accuracy: 0.9930 - val_loss: 0.0651 - val_accuracy: 0.9853\n",
+      "Epoch 93/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0989 - accuracy: 0.9897 - val_loss: 0.0819 - val_accuracy: 0.9938\n",
+      "Epoch 94/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0427 - accuracy: 0.9927 - val_loss: 0.0784 - val_accuracy: 0.9947\n",
+      "Epoch 95/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0463 - accuracy: 0.9938 - val_loss: 0.0942 - val_accuracy: 0.9933\n",
+      "Epoch 96/100\n",
+      "1364/1364 [==============================] - 13s 10ms/step - loss: 0.0598 - accuracy: 0.9908 - val_loss: 0.1309 - val_accuracy: 0.9909\n",
+      "Epoch 97/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0717 - accuracy: 0.9916 - val_loss: 0.0960 - val_accuracy: 0.9944\n",
+      "Epoch 98/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0370 - accuracy: 0.9938 - val_loss: 0.1445 - val_accuracy: 0.9947\n",
+      "Epoch 99/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0679 - accuracy: 0.9929 - val_loss: 0.1177 - val_accuracy: 0.9938\n",
+      "Epoch 100/100\n",
+      "1364/1364 [==============================] - 14s 10ms/step - loss: 0.0516 - accuracy: 0.9915 - val_loss: 0.1030 - val_accuracy: 0.9950\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x15b5fc310>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Train set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  1/107 [..............................] - ETA: 30s"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-14 11:04:43.450300: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "107/107 [==============================] - 1s 3ms/step\n",
+      "107/107 [==============================] - 0s 2ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[1037,    7,    1],\n",
+       "       [   0, 1208,    3],\n",
+       "       [   0,    6, 1146]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_x = model.predict(x_test) \n",
+    "y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "\n",
+    "y_pred = model.predict(x_test)\n",
+    "y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "confusion_matrix(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      1.00      1045\n",
+      "           1       0.99      1.00      0.99      1211\n",
+      "           2       1.00      0.99      1.00      1152\n",
+      "\n",
+      "    accuracy                           1.00      3408\n",
+      "   macro avg       1.00      0.99      1.00      3408\n",
+      "weighted avg       1.00      1.00      1.00      3408\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test_class, y_pred_class))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df = pd.read_csv(\"./test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"I\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"M\", \"label\"] = 1\n",
+    "test_df.loc[test_df[\"label\"] == \"D\", \"label\"] = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(input_scaler.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[267,   0,   0],\n",
+       "       [  0, 263,   0],\n",
+       "       [  0,   0, 299]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_x = model.predict(test_x) \n",
+    "y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "confusion_matrix(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00       267\n",
+      "           1       1.00      1.00      1.00       263\n",
+      "           2       1.00      1.00      1.00       299\n",
+      "\n",
+      "    accuracy                           1.00       829\n",
+      "   macro avg       1.00      1.00      1.00       829\n",
+      "weighted avg       1.00      1.00      1.00       829\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test_class, y_pred_class))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Dump Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://6d931dd9-8715-41d1-81d4-8f3b853c1109/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump the best model to a pickle file\n",
+    "with open(\"./model/lunge_model_deep_learning.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(model, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/4.stage.detection.ipynb b/core/lunge_model/4.stage.detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2029f73c84f71fd91df3801487c25588a009372d
--- /dev/null
+++ b/core/lunge_model/4.stage.detection.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[95030]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x111288860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x167476480). One of the two will be used. Which one is undefined.\n",
+      "objc[95030]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x110c50a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x1674764d0). One of the two will be used. Which one is undefined.\n",
+      "objc[95030]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x110c50a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x1674764f8). One of the two will be used. Which one is undefined.\n",
+      "objc[95030]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x110c50ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x167476520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import traceback\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Reconstruct input structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Set up important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ellipsis"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def extract_important_keypoints(results) -> list:\n",
+    "    '''\n",
+    "    Extract important keypoints from mediapipe pose detection\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "    data = []\n",
+    "    for lm in IMPORTANT_LMS:\n",
+    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "    \n",
+    "    return np.array(data).flatten().tolist()\n",
+    "\n",
+    "\n",
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
+    "    '''\n",
+    "    Calculate the angle between 3 points\n",
+    "    Unit of the angle will be in Degree\n",
+    "    '''\n",
+    "    point1 = np.array(point1)\n",
+    "    point2 = np.array(point2)\n",
+    "    point3 = np.array(point3)\n",
+    "\n",
+    "    # Calculate algo\n",
+    "    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
+    "    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
+    "\n",
+    "    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
+    "    return angleInDeg\n",
+    "    \n",
+    "\n",
+    "def analyze_knee_angle(\n",
+    "    mp_results, stage: str, angle_thresholds: list, draw_to_image: tuple = None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate angle of each knee while performer at the DOWN position\n",
+    "\n",
+    "    Return result explanation:\n",
+    "        error: True if at least 1 error\n",
+    "        right\n",
+    "            error: True if an error is on the right knee\n",
+    "            angle: Right knee angle\n",
+    "        left\n",
+    "            error: True if an error is on the left knee\n",
+    "            angle: Left knee angle\n",
+    "    \"\"\"\n",
+    "    results = {\n",
+    "        \"error\": None,\n",
+    "        \"right\": {\"error\": None, \"angle\": None},\n",
+    "        \"left\": {\"error\": None, \"angle\": None},\n",
+    "    }\n",
+    "\n",
+    "    landmarks = mp_results.pose_landmarks.landmark\n",
+    "\n",
+    "    # Calculate right knee angle\n",
+    "    right_hip = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y,\n",
+    "    ]\n",
+    "    right_knee = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y,\n",
+    "    ]\n",
+    "    right_ankle = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y,\n",
+    "    ]\n",
+    "    results[\"right\"][\"angle\"] = calculate_angle(right_hip, right_knee, right_ankle)\n",
+    "\n",
+    "    # Calculate left knee angle\n",
+    "    left_hip = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y,\n",
+    "    ]\n",
+    "    left_knee = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y,\n",
+    "    ]\n",
+    "    left_ankle = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y,\n",
+    "    ]\n",
+    "    results[\"left\"][\"angle\"] = calculate_angle(left_hip, left_knee, left_ankle)\n",
+    "\n",
+    "    # Draw to image\n",
+    "    if draw_to_image is not None and stage != \"down\":\n",
+    "        (image, video_dimensions) = draw_to_image\n",
+    "\n",
+    "        # Visualize angles\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"right\"][\"angle\"])),\n",
+    "            tuple(np.multiply(right_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            0.5,\n",
+    "            (255, 255, 255),\n",
+    "            1,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"left\"][\"angle\"])),\n",
+    "            tuple(np.multiply(left_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            0.5,\n",
+    "            (255, 255, 255),\n",
+    "            1,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "\n",
+    "    if stage != \"down\":\n",
+    "        return results\n",
+    "\n",
+    "    # Evaluation\n",
+    "    results[\"error\"] = False\n",
+    "\n",
+    "    if angle_thresholds[0] <= results[\"right\"][\"angle\"] <= angle_thresholds[1]:\n",
+    "        results[\"right\"][\"error\"] = False\n",
+    "    else:\n",
+    "        results[\"right\"][\"error\"] = True\n",
+    "        results[\"error\"] = True\n",
+    "\n",
+    "    if angle_thresholds[0] <= results[\"left\"][\"angle\"] <= angle_thresholds[1]:\n",
+    "        results[\"left\"][\"error\"] = False\n",
+    "    else:\n",
+    "        results[\"left\"][\"error\"] = True\n",
+    "        results[\"error\"] = True\n",
+    "\n",
+    "    # Draw to image\n",
+    "    if draw_to_image is not None:\n",
+    "        (image, video_dimensions) = draw_to_image\n",
+    "\n",
+    "        right_color = (255, 255, 255) if not results[\"right\"][\"error\"] else (0, 0, 255)\n",
+    "        left_color = (255, 255, 255) if not results[\"left\"][\"error\"] else (0, 0, 255)\n",
+    "\n",
+    "        right_font_scale = 0.5 if not results[\"right\"][\"error\"] else 1\n",
+    "        left_font_scale = 0.5 if not results[\"left\"][\"error\"] else 1\n",
+    "\n",
+    "        right_thickness = 1 if not results[\"right\"][\"error\"] else 2\n",
+    "        left_thickness = 1 if not results[\"left\"][\"error\"] else 2\n",
+    "\n",
+    "        # Visualize angles\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"right\"][\"angle\"])),\n",
+    "            tuple(np.multiply(right_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            right_font_scale,\n",
+    "            right_color,\n",
+    "            right_thickness,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"left\"][\"angle\"])),\n",
+    "            tuple(np.multiply(left_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            left_font_scale,\n",
+    "            left_color,\n",
+    "            left_thickness,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VIDEO_PATH1 = \"../data/lunge/lunge_test.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    input_scaler = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Detection with Sklearn model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/KNN_model.pkl\", \"rb\") as f:\n",
+    "    sklearn_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH1)\n",
+    "current_stage = \"\"\n",
+    "counter = 0\n",
+    "prediction_probability_threshold = 0.8\n",
+    "ANGLE_THRESHOLDS = [60, 135]\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            predicted_class = sklearn_model.predict(X)[0]\n",
+    "            prediction_probabilities = sklearn_model.predict_proba(X)[0]\n",
+    "            prediction_probability = round(prediction_probabilities[prediction_probabilities.argmax()], 2)\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if predicted_class == \"I\" and prediction_probability >= prediction_probability_threshold:\n",
+    "                current_stage = \"init\"\n",
+    "            elif predicted_class == \"M\" and prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"mid\"\n",
+    "            elif predicted_class == \"D\" and prediction_probability >= prediction_probability_threshold:\n",
+    "                if current_stage == \"mid\":\n",
+    "                    counter += 1\n",
+    "                \n",
+    "                current_stage = \"down\"\n",
+    "            \n",
+    "            # Error detection\n",
+    "            analyze_knee_angle(mp_results=results, stage=current_stage, angle_thresholds=ANGLE_THRESHOLDS, draw_to_image=(image, video_dimensions))\n",
+    "            \n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (400, 60), (245, 117, 16), -1)\n",
+    "\n",
+    "             # Display probability\n",
+    "            cv2.putText(image, \"PROB\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(prediction_probability), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display class\n",
+    "            cv2.putText(image, \"CLASS\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display probability\n",
+    "            cv2.putText(image, \"COUNTER\", (255, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(counter), (250, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "            traceback.print_exc()\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Detection with Deep learning model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-14 11:15:32.563689: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2022-11-14 11:15:32.564066: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/lunge_model_deep_learning.pkl\", \"rb\") as f:\n",
+    "    deep_learning_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 10ms/step\n",
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+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH1)\n",
+    "current_stage = \"\"\n",
+    "counter = 0\n",
+    "prediction_probability_threshold = 0.6\n",
+    "ANGLE_THRESHOLDS = [60, 135]\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row, ], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "            \n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            prediction = deep_learning_model.predict(X)\n",
+    "            predicted_class = np.argmax(prediction, axis=1)[0]\n",
+    "            prediction_probability = max(prediction.tolist()[0])\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if predicted_class == 0 and prediction_probability >= prediction_probability_threshold:\n",
+    "                current_stage = \"init\"\n",
+    "            elif predicted_class == 1 and prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"mid\"\n",
+    "            elif predicted_class == 2 and prediction_probability >= prediction_probability_threshold:\n",
+    "                if current_stage == \"mid\":\n",
+    "                    counter += 1\n",
+    "\n",
+    "                current_stage = \"down\"\n",
+    "            \n",
+    "            analyze_knee_angle(mp_results=results, stage=current_stage, angle_thresholds=ANGLE_THRESHOLDS, draw_to_image=(image, video_dimensions))\n",
+    "\n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (550, 60), (245, 117, 16), -1)\n",
+    "\n",
+    "            # # Display class\n",
+    "            cv2.putText(image, \"DETECTION\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # # Display probability\n",
+    "            cv2.putText(image, \"PROB\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(prediction_probability, 2)), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # # Display class\n",
+    "            cv2.putText(image, \"CLASS\", (225, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(predicted_class), (220, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # # Display class\n",
+    "            cv2.putText(image, \"COUNTER\", (350, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(counter), (345, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/5.err.data.ipynb b/core/lunge_model/5.err.data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3a3f8f86b4f903902ba4639435fa9c40f722dc07
--- /dev/null
+++ b/core/lunge_model/5.err.data.ipynb
@@ -0,0 +1,562 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[68038]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10c548860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x17b5da480). One of the two will be used. Which one is undefined.\n",
+      "objc[68038]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107304a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x17b5da4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[68038]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107304a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x17b5da4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[68038]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107304ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x17b5da520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "import os\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Build dataset from collected video"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Determine important landmarks and set up important functions\n",
+    "\n",
+    "The error that I try to tackle is **KNEE OVER TOE** error when lunge is at down stage.\n",
+    "- \"C\": Correct Form\n",
+    "- \"L\": Incorrect Form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "    \n",
+    "\n",
+    "def init_csv(dataset_path: str):\n",
+    "    '''\n",
+    "    Create a blank csv file with just columns\n",
+    "    '''\n",
+    "\n",
+    "    # Ignore if file is already exist\n",
+    "    if os.path.exists(dataset_path):\n",
+    "        return\n",
+    "\n",
+    "    # Write all the columns to a empty file\n",
+    "    with open(dataset_path, mode=\"w\", newline=\"\") as f:\n",
+    "        csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "        csv_writer.writerow(HEADERS)\n",
+    "\n",
+    "\n",
+    "def export_landmark_to_csv(dataset_path: str, results, action: str) -> None:\n",
+    "    '''\n",
+    "    Export Labeled Data from detected landmark to csv\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "    keypoints = []\n",
+    "\n",
+    "    try:\n",
+    "        # Extract coordinate of important landmarks\n",
+    "        for lm in IMPORTANT_LMS:\n",
+    "            keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "            keypoints.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "        \n",
+    "        keypoints = list(np.array(keypoints).flatten())\n",
+    "\n",
+    "        # Insert action as the label (first column)\n",
+    "        keypoints.insert(0, action)\n",
+    "\n",
+    "        # Append new row to .csv file\n",
+    "        with open(dataset_path, mode=\"a\", newline=\"\") as f:\n",
+    "            csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "            csv_writer.writerow(keypoints)\n",
+    "        \n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_dataset.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "    \n",
+    "\n",
+    "def concat_csv_files_with_same_headers(file_paths: list, saved_path: str):\n",
+    "    '''\n",
+    "    Concat different csv files\n",
+    "    '''\n",
+    "    all_df = []\n",
+    "    for path in file_paths:\n",
+    "        df = pd.read_csv(path, index_col=None, header=0)\n",
+    "        all_df.append(df)\n",
+    "    \n",
+    "    results = pd.concat(all_df, axis=0, ignore_index=True)\n",
+    "    results.to_csv(saved_path, sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
+    "    '''\n",
+    "    Calculate the angle between 3 points\n",
+    "    Unit of the angle will be in Degree\n",
+    "    '''\n",
+    "    point1 = np.array(point1)\n",
+    "    point2 = np.array(point2)\n",
+    "    point3 = np.array(point3)\n",
+    "\n",
+    "    # Calculate algo\n",
+    "    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
+    "    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
+    "\n",
+    "    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
+    "    return angleInDeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Extract data for train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OpenCV: Couldn't read video stream from file \"../data/lunge/lunge_9.mp4\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATASET_PATH = \"err.train.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/lunge/lunge_9.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.8, min_tracking_confidence=0.9) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "        \n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # * Press I to save as INIT stage\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # * Press M to save as MID stage\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 17907 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    9114\n",
+      "C    8793\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = describe_dataset(DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Extract data for test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TEST_DATASET_PATH = \"err.test.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/lunge/lunge_test_5.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "init_csv(TEST_DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.8) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "        \n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(10) & 0xFF\n",
+    "\n",
+    "        # * Press C to save as Correct stage\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # * Press L to save as Incorrect stage\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 783 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    406\n",
+      "C    377\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(TEST_DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 17907 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    9114\n",
+      "C    8793\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='label', ylabel='count'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(\"./err.train.csv\")\n",
+    "sns.countplot(x='label', data=df, palette=\"Set1\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='count', ylabel='label'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(y='label', data=test_df, palette=\"Set1\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/6.err.sklearn.ipynb b/core/lunge_model/6.err.sklearn.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..001d5a17f611c3fd8dcc95e7a676411c89bc2b9a
--- /dev/null
+++ b/core/lunge_model/6.err.sklearn.ipynb
@@ -0,0 +1,777 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[58344]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10ae08860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece480). One of the two will be used. Which one is undefined.\n",
+      "objc[58344]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105baca68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[58344]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105baca90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[58344]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x105bacab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Set up important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Describe and process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TRAIN_SET_PATH  = \"./err.train.csv\"\n",
+    "TEST_SET_PATH  = \"./err.test.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 17907 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    9114\n",
+      "C    8793\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>nose_x</th>\n",
+       "      <th>nose_y</th>\n",
+       "      <th>nose_z</th>\n",
+       "      <th>nose_v</th>\n",
+       "      <th>left_shoulder_x</th>\n",
+       "      <th>left_shoulder_y</th>\n",
+       "      <th>left_shoulder_z</th>\n",
+       "      <th>left_shoulder_v</th>\n",
+       "      <th>right_shoulder_x</th>\n",
+       "      <th>...</th>\n",
+       "      <th>right_heel_z</th>\n",
+       "      <th>right_heel_v</th>\n",
+       "      <th>left_foot_index_x</th>\n",
+       "      <th>left_foot_index_y</th>\n",
+       "      <th>left_foot_index_z</th>\n",
+       "      <th>left_foot_index_v</th>\n",
+       "      <th>right_foot_index_x</th>\n",
+       "      <th>right_foot_index_y</th>\n",
+       "      <th>right_foot_index_z</th>\n",
+       "      <th>right_foot_index_v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17904</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.647438</td>\n",
+       "      <td>0.442268</td>\n",
+       "      <td>0.004114</td>\n",
+       "      <td>0.999985</td>\n",
+       "      <td>0.615798</td>\n",
+       "      <td>0.517170</td>\n",
+       "      <td>0.151706</td>\n",
+       "      <td>0.999579</td>\n",
+       "      <td>0.631354</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.034228</td>\n",
+       "      <td>0.979719</td>\n",
+       "      <td>0.701826</td>\n",
+       "      <td>0.880516</td>\n",
+       "      <td>0.134222</td>\n",
+       "      <td>0.979319</td>\n",
+       "      <td>0.504880</td>\n",
+       "      <td>0.881748</td>\n",
+       "      <td>-0.027911</td>\n",
+       "      <td>0.986165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17905</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.649652</td>\n",
+       "      <td>0.419057</td>\n",
+       "      <td>0.008783</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>0.617577</td>\n",
+       "      <td>0.503514</td>\n",
+       "      <td>0.158545</td>\n",
+       "      <td>0.999529</td>\n",
+       "      <td>0.631972</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.061176</td>\n",
+       "      <td>0.980431</td>\n",
+       "      <td>0.704606</td>\n",
+       "      <td>0.880248</td>\n",
+       "      <td>0.071476</td>\n",
+       "      <td>0.979932</td>\n",
+       "      <td>0.504513</td>\n",
+       "      <td>0.881766</td>\n",
+       "      <td>-0.088832</td>\n",
+       "      <td>0.986975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17906</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653556</td>\n",
+       "      <td>0.400394</td>\n",
+       "      <td>0.014852</td>\n",
+       "      <td>0.999980</td>\n",
+       "      <td>0.620734</td>\n",
+       "      <td>0.486522</td>\n",
+       "      <td>0.169807</td>\n",
+       "      <td>0.999556</td>\n",
+       "      <td>0.631171</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.138678</td>\n",
+       "      <td>0.979078</td>\n",
+       "      <td>0.705475</td>\n",
+       "      <td>0.878981</td>\n",
+       "      <td>0.003690</td>\n",
+       "      <td>0.979199</td>\n",
+       "      <td>0.504067</td>\n",
+       "      <td>0.882642</td>\n",
+       "      <td>-0.183304</td>\n",
+       "      <td>0.986824</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      label    nose_x    nose_y    nose_z    nose_v  left_shoulder_x  \\\n",
+       "17904     1  0.647438  0.442268  0.004114  0.999985         0.615798   \n",
+       "17905     1  0.649652  0.419057  0.008783  0.999983         0.617577   \n",
+       "17906     1  0.653556  0.400394  0.014852  0.999980         0.620734   \n",
+       "\n",
+       "       left_shoulder_y  left_shoulder_z  left_shoulder_v  right_shoulder_x  \\\n",
+       "17904         0.517170         0.151706         0.999579          0.631354   \n",
+       "17905         0.503514         0.158545         0.999529          0.631972   \n",
+       "17906         0.486522         0.169807         0.999556          0.631171   \n",
+       "\n",
+       "       ...  right_heel_z  right_heel_v  left_foot_index_x  left_foot_index_y  \\\n",
+       "17904  ...     -0.034228      0.979719           0.701826           0.880516   \n",
+       "17905  ...     -0.061176      0.980431           0.704606           0.880248   \n",
+       "17906  ...     -0.138678      0.979078           0.705475           0.878981   \n",
+       "\n",
+       "       left_foot_index_z  left_foot_index_v  right_foot_index_x  \\\n",
+       "17904           0.134222           0.979319            0.504880   \n",
+       "17905           0.071476           0.979932            0.504513   \n",
+       "17906           0.003690           0.979199            0.504067   \n",
+       "\n",
+       "       right_foot_index_y  right_foot_index_z  right_foot_index_v  \n",
+       "17904            0.881748           -0.027911            0.986165  \n",
+       "17905            0.881766           -0.088832            0.986975  \n",
+       "17906            0.882642           -0.183304            0.986824  \n",
+       "\n",
+       "[3 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(TRAIN_SET_PATH)\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 1\n",
+    "\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract features and class\n",
+    "X = df.drop(\"label\", axis=1)\n",
+    "y = df[\"label\"].astype(\"int\")\n",
+    "\n",
+    "X = pd.DataFrame(sc.transform(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10827    0\n",
+       "11395    0\n",
+       "3742     1\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
+    "y_test.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Train & Evaluate Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Train and evaluate model with train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[1.0, 0.999]</td>\n",
+       "      <td>0.999721</td>\n",
+       "      <td>[0.999, 1.0]</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[[1713, 1], [0, 1868]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[1.0, 0.998]</td>\n",
+       "      <td>0.999162</td>\n",
+       "      <td>[0.998, 1.0]</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>[[1711, 3], [0, 1868]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>0.999162</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>[0.999, 0.999]</td>\n",
+       "      <td>[[1712, 2], [1, 1867]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.997, 0.997]</td>\n",
+       "      <td>0.997208</td>\n",
+       "      <td>[0.997, 0.997]</td>\n",
+       "      <td>[0.997, 0.997]</td>\n",
+       "      <td>[[1709, 5], [5, 1863]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.992, 0.987]</td>\n",
+       "      <td>0.989391</td>\n",
+       "      <td>[0.986, 0.993]</td>\n",
+       "      <td>[0.989, 0.99]</td>\n",
+       "      <td>[[1690, 24], [14, 1854]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.992, 0.988]</td>\n",
+       "      <td>0.989950</td>\n",
+       "      <td>[0.987, 0.993]</td>\n",
+       "      <td>[0.989, 0.99]</td>\n",
+       "      <td>[[1692, 22], [14, 1854]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.963, 0.952]</td>\n",
+       "      <td>0.957286</td>\n",
+       "      <td>[0.947, 0.967]</td>\n",
+       "      <td>[0.955, 0.959]</td>\n",
+       "      <td>[[1623, 91], [62, 1806]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model Precision Score  Accuracy score    Recall Score        F1 score  \\\n",
+       "0   SVC    [1.0, 0.999]        0.999721    [0.999, 1.0]      [1.0, 1.0]   \n",
+       "1   KNN    [1.0, 0.998]        0.999162    [0.998, 1.0]  [0.999, 0.999]   \n",
+       "2    RF  [0.999, 0.999]        0.999162  [0.999, 0.999]  [0.999, 0.999]   \n",
+       "3   DTC  [0.997, 0.997]        0.997208  [0.997, 0.997]  [0.997, 0.997]   \n",
+       "4    LR  [0.992, 0.987]        0.989391  [0.986, 0.993]   [0.989, 0.99]   \n",
+       "5  SGDC  [0.992, 0.988]        0.989950  [0.987, 0.993]   [0.989, 0.99]   \n",
+       "6    NB  [0.963, 0.952]        0.957286  [0.947, 0.967]  [0.955, 0.959]   \n",
+       "\n",
+       "           Confusion Matrix  \n",
+       "0    [[1713, 1], [0, 1868]]  \n",
+       "1    [[1711, 3], [0, 1868]]  \n",
+       "2    [[1712, 2], [1, 1867]]  \n",
+       "3    [[1709, 5], [5, 1863]]  \n",
+       "4  [[1690, 24], [14, 1854]]  \n",
+       "5  [[1692, 22], [14, 1854]]  \n",
+       "6  [[1623, 91], [62, 1806]]  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algorithms =[(\"LR\", LogisticRegression()),\n",
+    "         (\"SVC\", SVC(probability=True)),\n",
+    "         ('KNN',KNeighborsClassifier()),\n",
+    "         (\"DTC\", DecisionTreeClassifier()),\n",
+    "         (\"SGDC\", CalibratedClassifierCV(SGDClassifier())),\n",
+    "         (\"NB\", GaussianNB()),\n",
+    "         ('RF', RandomForestClassifier()),]\n",
+    "\n",
+    "models = {}\n",
+    "final_results = []\n",
+    "\n",
+    "for name, model in algorithms:\n",
+    "    trained_model = model.fit(X_train, y_train)\n",
+    "    models[name] = trained_model\n",
+    "\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(X_test)\n",
+    "\n",
+    "    p_score = precision_score(y_test, model_results, average=None, labels=[1, 0])\n",
+    "    a_score = accuracy_score(y_test, model_results)\n",
+    "    r_score = recall_score(y_test, model_results, average=None, labels=[1, 0])\n",
+    "    f1_score_result = f1_score(y_test, model_results, average=None, labels=[1, 0])\n",
+    "    cm = confusion_matrix(y_test, model_results, labels=[1, 0])\n",
+    "    final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm))\n",
+    "\n",
+    "# Sort results by F1 score\n",
+    "final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 1107 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    561\n",
+      "C    546\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(TEST_SET_PATH)\n",
+    "test_df = test_df.sample(frac=1).reset_index(drop=True)\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 1\n",
+    "\n",
+    "test_x = test_df.drop(\"label\", axis=1)\n",
+    "test_y = test_df[\"label\"].astype(\"int\")\n",
+    "\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.948, 0.998]</td>\n",
+       "      <td>0.971996</td>\n",
+       "      <td>[0.998, 0.947]</td>\n",
+       "      <td>[0.972, 0.972]</td>\n",
+       "      <td>[[545, 1], [30, 531]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.922, 0.998]</td>\n",
+       "      <td>0.957543</td>\n",
+       "      <td>[0.998, 0.918]</td>\n",
+       "      <td>[0.959, 0.956]</td>\n",
+       "      <td>[[545, 1], [46, 515]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.95, 0.889]</td>\n",
+       "      <td>0.916893</td>\n",
+       "      <td>[0.877, 0.955]</td>\n",
+       "      <td>[0.912, 0.921]</td>\n",
+       "      <td>[[479, 67], [25, 536]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.786, 0.921]</td>\n",
+       "      <td>0.841915</td>\n",
+       "      <td>[0.934, 0.752]</td>\n",
+       "      <td>[0.854, 0.828]</td>\n",
+       "      <td>[[510, 36], [139, 422]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.79, 0.751]</td>\n",
+       "      <td>0.768744</td>\n",
+       "      <td>[0.723, 0.813]</td>\n",
+       "      <td>[0.755, 0.781]</td>\n",
+       "      <td>[[395, 151], [105, 456]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[0.737, 0.799]</td>\n",
+       "      <td>0.765131</td>\n",
+       "      <td>[0.815, 0.717]</td>\n",
+       "      <td>[0.774, 0.756]</td>\n",
+       "      <td>[[445, 101], [159, 402]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[0.659, 0.842]</td>\n",
+       "      <td>0.719964</td>\n",
+       "      <td>[0.894, 0.551]</td>\n",
+       "      <td>[0.759, 0.666]</td>\n",
+       "      <td>[[488, 58], [252, 309]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model Precision Score  Accuracy score    Recall Score        F1 score  \\\n",
+       "0    LR  [0.948, 0.998]        0.971996  [0.998, 0.947]  [0.972, 0.972]   \n",
+       "1  SGDC  [0.922, 0.998]        0.957543  [0.998, 0.918]  [0.959, 0.956]   \n",
+       "2   DTC   [0.95, 0.889]        0.916893  [0.877, 0.955]  [0.912, 0.921]   \n",
+       "3    RF  [0.786, 0.921]        0.841915  [0.934, 0.752]  [0.854, 0.828]   \n",
+       "4    NB   [0.79, 0.751]        0.768744  [0.723, 0.813]  [0.755, 0.781]   \n",
+       "5   KNN  [0.737, 0.799]        0.765131  [0.815, 0.717]  [0.774, 0.756]   \n",
+       "6   SVC  [0.659, 0.842]        0.719964  [0.894, 0.551]  [0.759, 0.666]   \n",
+       "\n",
+       "           Confusion Matrix  \n",
+       "0     [[545, 1], [30, 531]]  \n",
+       "1     [[545, 1], [46, 515]]  \n",
+       "2    [[479, 67], [25, 536]]  \n",
+       "3   [[510, 36], [139, 422]]  \n",
+       "4  [[395, 151], [105, 456]]  \n",
+       "5  [[445, 101], [159, 402]]  \n",
+       "6   [[488, 58], [252, 309]]  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=None, labels=[1, 0])\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=None, labels=[1, 0])\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=None, labels=[1, 0])\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[1, 0])\n",
+    "    testset_final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm ))\n",
+    "\n",
+    "\n",
+    "testset_final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Dump Models \n",
+    "\n",
+    "According to the evaluation above, LR and KNN SGDC would be chosen for more eval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/err_all_sklearn.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/err_SGDC_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"SGDC\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn/err_LR_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"LR\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/7.err.deep_learning.ipynb b/core/lunge_model/7.err.deep_learning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..32b91b94bcea4691b6916370df3818d7fa1b4dcb
--- /dev/null
+++ b/core/lunge_model/7.err.deep_learning.ipynb
@@ -0,0 +1,1366 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data visualization\n",
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "\n",
+    "# Keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "from keras.callbacks import EarlyStopping\n",
+    "import keras_tuner as kt\n",
+    "\n",
+    "# Train-Test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# Classification Report\n",
+    "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Set up important landmarks and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]\n",
+    "\n",
+    "TRAIN_SET_PATH  = \"./err.train.csv\"\n",
+    "TEST_SET_PATH  = \"./err.test.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "# Remove duplicate rows (optional)\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_dataset.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Describe and process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 17907 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    9114\n",
+      "C    8793\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "df = describe_dataset(TRAIN_SET_PATH)\n",
+    "\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f2:\n",
+    "    input_scaler = pickle.load(f2)\n",
+    "\n",
+    "x = df.drop(\"label\", axis = 1)\n",
+    "x = pd.DataFrame(input_scaler.transform(x))\n",
+    "\n",
+    "y = df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Train model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Set up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop_early = EarlyStopping(monitor='loss', patience=3)\n",
+    "\n",
+    "# Final Results\n",
+    "final_models = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_model(model):\n",
+    "    '''\n",
+    "    Describe Model architecture\n",
+    "    '''\n",
+    "    print(f\"Describe models architecture\")\n",
+    "    for i, layer in enumerate(model.layers):\n",
+    "        number_of_units = layer.units if hasattr(layer, 'units') else 0\n",
+    "\n",
+    "        if hasattr(layer, \"activation\"):\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: \", layer.activation)\n",
+    "        else:\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: None\")\n",
+    "            \n",
+    "\n",
+    "def get_best_model(tuner):\n",
+    "    '''\n",
+    "    Describe and return the best model found from keras tuner\n",
+    "    '''\n",
+    "    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+    "    best_model = tuner.hypermodel.build(best_hps)\n",
+    "\n",
+    "    describe_model(best_model)\n",
+    "\n",
+    "    for h_param in [\"learning_rate\"]:\n",
+    "        print(f\"{h_param}: {tuner.get_best_hyperparameters()[0].get(h_param)}\")\n",
+    "    \n",
+    "    return best_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Model with 3 layers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(52, input_dim = 52, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation='softmax'))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Reloading Oracle from existing project keras_tuner_dir/keras_tuner_demo/oracle.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner = kt.Hyperband(\n",
+    "    model_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 38s]\n",
+      "accuracy: 0.9995812177658081\n",
+      "\n",
+      "Best accuracy So Far: 1.0000001192092896\n",
+      "Total elapsed time: 00h 08m 17s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-2: 192 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-3: 2 units, func:  <function softmax at 0x155f86040>\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n",
+      "  12/1433 [..............................] - ETA: 13s - loss: 0.5386 - accuracy: 0.7000"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 10:49:54.623575: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1429/1433 [============================>.] - ETA: 0s - loss: 0.0323 - accuracy: 0.9883"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 10:50:06.095468: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - 14s 9ms/step - loss: 0.0322 - accuracy: 0.9883 - val_loss: 0.0022 - val_accuracy: 0.9992\n",
+      "Epoch 2/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 0.0048 - accuracy: 0.9984 - val_loss: 0.0103 - val_accuracy: 0.9964\n",
+      "Epoch 3/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0018 - val_accuracy: 0.9994\n",
+      "Epoch 4/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 8.9034e-04 - val_accuracy: 0.9997\n",
+      "Epoch 5/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 6.6072e-04 - val_accuracy: 0.9997\n",
+      "Epoch 6/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0011 - val_accuracy: 0.9997\n",
+      "Epoch 7/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0013 - val_accuracy: 0.9997\n",
+      "Epoch 8/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 6.2126e-04 - val_accuracy: 0.9997\n",
+      "Epoch 9/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 3.3005e-04 - val_accuracy: 0.9997\n",
+      "Epoch 10/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 3.4855e-04 - val_accuracy: 0.9997\n",
+      "Epoch 11/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 4.9058e-04 - accuracy: 0.9999 - val_loss: 7.0838e-04 - val_accuracy: 0.9997\n",
+      "Epoch 12/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 3.1028e-05 - accuracy: 1.0000 - val_loss: 9.7843e-05 - val_accuracy: 1.0000\n",
+      "Epoch 13/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0037 - val_accuracy: 0.9986\n",
+      "Epoch 14/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 6.7057e-04 - accuracy: 0.9998 - val_loss: 4.5239e-04 - val_accuracy: 0.9997\n",
+      "Epoch 15/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 3.3223e-05 - accuracy: 1.0000 - val_loss: 3.2696e-04 - val_accuracy: 0.9997\n",
+      "Epoch 16/100\n",
+      "1433/1433 [==============================] - 13s 9ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 1.6425e-04 - val_accuracy: 1.0000\n",
+      "Epoch 17/100\n",
+      "1433/1433 [==============================] - 14s 10ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0011 - val_accuracy: 0.9997\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x159960100>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = get_best_model(tuner)\n",
+    "model.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"3_layers\"] = model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Model with 5 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_builder_5(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(52, input_dim = 52, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation='softmax'))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 44s]\n",
+      "accuracy: 0.9998604655265808\n",
+      "\n",
+      "Best accuracy So Far: 1.0000001192092896\n",
+      "Total elapsed time: 00h 08m 57s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner = kt.Hyperband(\n",
+    "    model_builder_5,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_2'\n",
+    ")\n",
+    "tuner.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-2: 480 units, func:  <function tanh at 0x155f86dc0>\n",
+      "Layer-3: 480 units, func:  <function tanh at 0x155f86dc0>\n",
+      "Layer-4: 192 units, func:  <function tanh at 0x155f86dc0>\n",
+      "Layer-5: 2 units, func:  <function softmax at 0x155f86040>\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 11:11:48.332514: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - ETA: 0s - loss: 0.0428 - accuracy: 0.9872"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 11:12:02.217934: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0428 - accuracy: 0.9872 - val_loss: 0.0248 - val_accuracy: 0.9913\n",
+      "Epoch 2/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0055 - accuracy: 0.9986 - val_loss: 0.0016 - val_accuracy: 0.9992\n",
+      "Epoch 3/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 0.0036 - val_accuracy: 0.9992\n",
+      "Epoch 4/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0017 - val_accuracy: 0.9997\n",
+      "Epoch 5/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0048 - accuracy: 0.9982 - val_loss: 0.0019 - val_accuracy: 0.9994\n",
+      "Epoch 6/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 8.5989e-04 - val_accuracy: 0.9997\n",
+      "Epoch 7/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 4.7530e-04 - val_accuracy: 0.9997\n",
+      "Epoch 8/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0022 - accuracy: 0.9991 - val_loss: 3.3401e-04 - val_accuracy: 0.9997\n",
+      "Epoch 9/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0018 - accuracy: 0.9993 - val_loss: 3.5316e-04 - val_accuracy: 0.9997\n",
+      "Epoch 10/100\n",
+      "1433/1433 [==============================] - 18s 13ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 5.9313e-04 - val_accuracy: 0.9997\n",
+      "Epoch 11/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 5.0026e-04 - val_accuracy: 0.9997\n",
+      "Epoch 12/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 2.7773e-04 - val_accuracy: 0.9997\n",
+      "Epoch 13/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 3.9777e-04 - accuracy: 0.9999 - val_loss: 2.6341e-04 - val_accuracy: 0.9997\n",
+      "Epoch 14/100\n",
+      "1433/1433 [==============================] - 18s 12ms/step - loss: 0.0021 - accuracy: 0.9997 - val_loss: 3.1135e-04 - val_accuracy: 1.0000\n",
+      "Epoch 15/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 2.4923e-04 - accuracy: 0.9999 - val_loss: 3.7806e-04 - val_accuracy: 0.9997\n",
+      "Epoch 16/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 4.5137e-04 - accuracy: 0.9999 - val_loss: 1.0070e-04 - val_accuracy: 1.0000\n",
+      "Epoch 17/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 8.3379e-06 - accuracy: 1.0000 - val_loss: 6.9653e-04 - val_accuracy: 0.9997\n",
+      "Epoch 18/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 1.4246e-04 - val_accuracy: 1.0000\n",
+      "Epoch 19/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 2.8542e-05 - accuracy: 1.0000 - val_loss: 1.0260e-04 - val_accuracy: 1.0000\n",
+      "Epoch 20/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 2.7252e-04 - val_accuracy: 1.0000\n",
+      "Epoch 21/100\n",
+      "1433/1433 [==============================] - 16s 11ms/step - loss: 3.2176e-05 - accuracy: 1.0000 - val_loss: 1.3285e-04 - val_accuracy: 1.0000\n",
+      "Epoch 22/100\n",
+      "1433/1433 [==============================] - 18s 12ms/step - loss: 1.2290e-05 - accuracy: 1.0000 - val_loss: 1.1766e-04 - val_accuracy: 1.0000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x2f26db9d0>"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_5 = get_best_model(tuner)\n",
+    "model_5.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"5_layers\"] = model_5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4. Model with 7 layers along with Dropout layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_builder_dropout_5(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(52, input_dim = 52, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation='softmax'))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 53s]\n",
+      "accuracy: 0.9993019700050354\n",
+      "\n",
+      "Best accuracy So Far: 0.9997208118438721\n",
+      "Total elapsed time: 00h 11m 19s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner = kt.Hyperband(\n",
+    "    model_builder_dropout_5,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_3'\n",
+    ")\n",
+    "tuner.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-2: 288 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 224 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 320 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x155f86040>\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 11:34:38.687622: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - ETA: 0s - loss: 0.0443 - accuracy: 0.9823"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 11:34:54.587049: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - 19s 13ms/step - loss: 0.0443 - accuracy: 0.9823 - val_loss: 0.0023 - val_accuracy: 0.9994\n",
+      "Epoch 2/100\n",
+      "1433/1433 [==============================] - 18s 12ms/step - loss: 0.0090 - accuracy: 0.9976 - val_loss: 0.0046 - val_accuracy: 0.9975\n",
+      "Epoch 3/100\n",
+      "1433/1433 [==============================] - 18s 13ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0011 - val_accuracy: 0.9994\n",
+      "Epoch 4/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0073 - accuracy: 0.9983 - val_loss: 0.0015 - val_accuracy: 0.9994\n",
+      "Epoch 5/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 8.3813e-04 - val_accuracy: 0.9997\n",
+      "Epoch 6/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0053 - accuracy: 0.9987 - val_loss: 0.0047 - val_accuracy: 0.9986\n",
+      "Epoch 7/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0044 - accuracy: 0.9984 - val_loss: 9.9630e-04 - val_accuracy: 0.9997\n",
+      "Epoch 8/100\n",
+      "1433/1433 [==============================] - 18s 13ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 0.0013 - val_accuracy: 0.9994\n",
+      "Epoch 9/100\n",
+      "1433/1433 [==============================] - 18s 13ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.0074 - val_accuracy: 0.9989\n",
+      "Epoch 10/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.0018 - val_accuracy: 0.9997\n",
+      "Epoch 11/100\n",
+      "1433/1433 [==============================] - 18s 12ms/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 3.8607e-04 - val_accuracy: 0.9997\n",
+      "Epoch 12/100\n",
+      "1433/1433 [==============================] - 17s 12ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.0011 - val_accuracy: 0.9994\n",
+      "Epoch 13/100\n",
+      "1433/1433 [==============================] - 18s 12ms/step - loss: 0.0048 - accuracy: 0.9990 - val_loss: 1.1490e-04 - val_accuracy: 1.0000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x2f45e9280>"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_5_with_dropout = get_best_model(tuner)\n",
+    "model_5_with_dropout.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['7_layers_with_dropout', '5_layers', '3_layers'])"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_models[\"7_layers_with_dropout\"] = model_5_with_dropout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5. Model with 7 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_builder_7(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(52, input_dim = 52, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_4 = hp.Int('layer_4', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_5 = hp.Int('layer_5', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_4, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_5, activation=hp_activation))\n",
+    "    model.add(Dense(2, activation='softmax'))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 52s]\n",
+      "accuracy: 0.9996510148048401\n",
+      "\n",
+      "Best accuracy So Far: 1.0000001192092896\n",
+      "Total elapsed time: 00h 12m 14s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner = kt.Hyperband(\n",
+    "    model_builder_7,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_4'\n",
+    ")\n",
+    "tuner.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-2: 32 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-4: 192 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-5: 224 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-6: 416 units, func:  <function relu at 0x155f86a60>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x155f86040>\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 13:44:16.853169: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - ETA: 0s - loss: 0.0618 - accuracy: 0.9774"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 13:44:35.283222: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1433/1433 [==============================] - 22s 15ms/step - loss: 0.0618 - accuracy: 0.9774 - val_loss: 0.0034 - val_accuracy: 0.9986\n",
+      "Epoch 2/100\n",
+      "1433/1433 [==============================] - 22s 15ms/step - loss: 0.0059 - accuracy: 0.9985 - val_loss: 0.0092 - val_accuracy: 0.9969\n",
+      "Epoch 3/100\n",
+      "1433/1433 [==============================] - 22s 15ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.0018 - val_accuracy: 0.9997\n",
+      "Epoch 4/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.0017 - val_accuracy: 0.9992\n",
+      "Epoch 5/100\n",
+      "1433/1433 [==============================] - 21s 14ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 3.8267e-04 - val_accuracy: 1.0000\n",
+      "Epoch 6/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 2.8054e-04 - val_accuracy: 1.0000\n",
+      "Epoch 7/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0038 - val_accuracy: 0.9983\n",
+      "Epoch 8/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 1.5269e-04 - val_accuracy: 1.0000\n",
+      "Epoch 9/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 4.1306e-04 - val_accuracy: 0.9997\n",
+      "Epoch 10/100\n",
+      "1433/1433 [==============================] - 21s 14ms/step - loss: 2.9369e-04 - accuracy: 0.9999 - val_loss: 1.3631e-05 - val_accuracy: 1.0000\n",
+      "Epoch 11/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 1.4331e-05 - accuracy: 1.0000 - val_loss: 6.5710e-06 - val_accuracy: 1.0000\n",
+      "Epoch 12/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 1.8750e-04 - val_accuracy: 1.0000\n",
+      "Epoch 13/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 3.1710e-04 - accuracy: 0.9998 - val_loss: 4.7737e-05 - val_accuracy: 1.0000\n",
+      "Epoch 14/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 1.3424e-05 - accuracy: 1.0000 - val_loss: 1.4000e-05 - val_accuracy: 1.0000\n",
+      "Epoch 15/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 3.3917e-06 - accuracy: 1.0000 - val_loss: 1.4799e-05 - val_accuracy: 1.0000\n",
+      "Epoch 16/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 1.1946e-06 - accuracy: 1.0000 - val_loss: 7.0157e-06 - val_accuracy: 1.0000\n",
+      "Epoch 17/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 6.1554e-07 - accuracy: 1.0000 - val_loss: 2.0066e-05 - val_accuracy: 1.0000\n",
+      "Epoch 18/100\n",
+      "1433/1433 [==============================] - 21s 14ms/step - loss: 2.6470e-07 - accuracy: 1.0000 - val_loss: 5.7550e-05 - val_accuracy: 1.0000\n",
+      "Epoch 19/100\n",
+      "1433/1433 [==============================] - 21s 14ms/step - loss: 1.2669e-07 - accuracy: 1.0000 - val_loss: 4.0596e-06 - val_accuracy: 1.0000\n",
+      "Epoch 20/100\n",
+      "1433/1433 [==============================] - 22s 16ms/step - loss: 6.8169e-08 - accuracy: 1.0000 - val_loss: 1.9726e-05 - val_accuracy: 1.0000\n",
+      "Epoch 21/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 4.3585e-08 - accuracy: 1.0000 - val_loss: 9.4645e-05 - val_accuracy: 1.0000\n",
+      "Epoch 22/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 0.0042 - accuracy: 0.9994 - val_loss: 0.0031 - val_accuracy: 0.9992\n",
+      "Epoch 23/100\n",
+      "1433/1433 [==============================] - 21s 14ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 2.0752e-05 - val_accuracy: 1.0000\n",
+      "Epoch 24/100\n",
+      "1433/1433 [==============================] - 21s 15ms/step - loss: 1.0676e-04 - accuracy: 0.9999 - val_loss: 1.2278e-05 - val_accuracy: 1.0000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x326e838b0>"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_7 = get_best_model(tuner)\n",
+    "model_7.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"7_layers\"] = model_7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.6. Final Models Description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7_layers_with_dropout: Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-2: 288 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 224 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 320 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x15db71160>\n",
+      "\n",
+      "5_layers: Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-2: 480 units, func:  <function tanh at 0x15db71ee0>\n",
+      "Layer-3: 480 units, func:  <function tanh at 0x15db71ee0>\n",
+      "Layer-4: 192 units, func:  <function tanh at 0x15db71ee0>\n",
+      "Layer-5: 2 units, func:  <function softmax at 0x15db71160>\n",
+      "\n",
+      "3_layers: Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-2: 192 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-3: 2 units, func:  <function softmax at 0x15db71160>\n",
+      "\n",
+      "7_layers: Describe models architecture\n",
+      "Layer-1: 52 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-2: 32 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-4: 192 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-5: 224 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-6: 416 units, func:  <function relu at 0x15db71b80>\n",
+      "Layer-7: 2 units, func:  <function softmax at 0x15db71160>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, model in final_models.items():\n",
+    "    print(f\"{name}: \", end=\"\")\n",
+    "    describe_model(model)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0]</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[[1805, 1], [0, 1776]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0]</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[[1805, 1], [0, 1776]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[1.0, 1.0]</td>\n",
+       "      <td>[[1806, 0], [0, 1776]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[1.0, 0.994]</td>\n",
+       "      <td>[0.994, 1.0]</td>\n",
+       "      <td>[0.997, 0.997]</td>\n",
+       "      <td>[[1795, 11], [0, 1776]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model Precision Score  Recall Score        F1 score  \\\n",
+       "0  7_layers_with_dropout    [1.0, 0.999]  [0.999, 1.0]      [1.0, 1.0]   \n",
+       "1               3_layers    [1.0, 0.999]  [0.999, 1.0]      [1.0, 1.0]   \n",
+       "2               7_layers      [1.0, 1.0]    [1.0, 1.0]      [1.0, 1.0]   \n",
+       "3               5_layers    [1.0, 0.994]  [0.994, 1.0]  [0.997, 0.997]   \n",
+       "\n",
+       "          Confusion Matrix  \n",
+       "0   [[1805, 1], [0, 1776]]  \n",
+       "1   [[1805, 1], [0, 1776]]  \n",
+       "2   [[1806, 0], [0, 1776]]  \n",
+       "3  [[1795, 11], [0, 1776]]  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(x_test, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    \n",
+    "    train_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "train_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
+    "pd.DataFrame(train_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2. Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df = pd.read_csv(TEST_SET_PATH)\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(input_scaler.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-25 15:46:42.537983: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:46:42.694947: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:46:42.853052: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-25 15:46:42.974234: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[0.998, 0.873]</td>\n",
+       "      <td>[0.859, 0.998]</td>\n",
+       "      <td>[0.923, 0.932]</td>\n",
+       "      <td>[[482, 79], [1, 545]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[0.995, 0.786]</td>\n",
+       "      <td>[0.736, 0.996]</td>\n",
+       "      <td>[0.846, 0.879]</td>\n",
+       "      <td>[[413, 148], [2, 544]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[0.963, 0.755]</td>\n",
+       "      <td>[0.693, 0.973]</td>\n",
+       "      <td>[0.806, 0.85]</td>\n",
+       "      <td>[[389, 172], [15, 531]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[0.984, 0.687]</td>\n",
+       "      <td>[0.561, 0.991]</td>\n",
+       "      <td>[0.715, 0.812]</td>\n",
+       "      <td>[[315, 246], [5, 541]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model Precision Score    Recall Score        F1 score  \\\n",
+       "0               3_layers  [0.998, 0.873]  [0.859, 0.998]  [0.923, 0.932]   \n",
+       "1  7_layers_with_dropout  [0.995, 0.786]  [0.736, 0.996]  [0.846, 0.879]   \n",
+       "2               5_layers  [0.963, 0.755]  [0.693, 0.973]   [0.806, 0.85]   \n",
+       "3               7_layers  [0.984, 0.687]  [0.561, 0.991]  [0.715, 0.812]   \n",
+       "\n",
+       "          Confusion Matrix  \n",
+       "0    [[482, 79], [1, 545]]  \n",
+       "1   [[413, 148], [2, 544]]  \n",
+       "2  [[389, 172], [15, 531]]  \n",
+       "3   [[315, 246], [5, 541]]  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1])\n",
+    "    \n",
+    "    test_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "test_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
+    "pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Dump Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://4444bb6e-de2c-4bed-806c-db9d955522ad/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump the best model to a pickle file\n",
+    "with open(\"./model/dp/err_lunge_dp.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models[\"3_layers\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://0f5761b9-5d62-4bf6-8de5-34355293f17a/assets\n",
+      "INFO:tensorflow:Assets written to: ram://ac2add31-fb1f-4baa-b925-718896fea315/assets\n",
+      "INFO:tensorflow:Assets written to: ram://20b6d7c9-7ddf-42a1-aaba-c31e12762a10/assets\n",
+      "INFO:tensorflow:Assets written to: ram://ee8c66ec-0ee3-4a5c-bc03-f12225018c47/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"./model/dp/all_models.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/8.err.evaluation.ipynb b/core/lunge_model/8.err.evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0c8e0458b62fe63dfc2c52f1c2580663585b2fd1
--- /dev/null
+++ b/core/lunge_model/8.err.evaluation.ipynb
@@ -0,0 +1,908 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix, roc_curve, auc\n",
+    "\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Set ups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Load models & scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-27 13:04:59.224051: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2022-11-27 13:04:59.224496: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load all sklearn models\n",
+    "with open(\"./model/sklearn/err_all_sklearn.pkl\", \"rb\") as f:\n",
+    "    sklearn_models = pickle.load(f)\n",
+    "\n",
+    "# Load all deep learning models\n",
+    "with open(\"./model/dp/all_models.pkl\", \"rb\") as f:\n",
+    "    dp_models = pickle.load(f)\n",
+    "\n",
+    "# Load input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Process Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 1107 \n",
+      "Number of columns: 53\n",
+      "\n",
+      "Labels: \n",
+      "L    561\n",
+      "C    546\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "test_df = describe_dataset(\"./err.test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 1\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 0\n",
+    "\n",
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"].astype('int')\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Test set evaluation for all models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Sklearn models evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.973314</td>\n",
+       "      <td>0.971996</td>\n",
+       "      <td>0.971996</td>\n",
+       "      <td>0.971987</td>\n",
+       "      <td>[[545, 1], [30, 531]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.751947</td>\n",
+       "      <td>0.719964</td>\n",
+       "      <td>0.719964</td>\n",
+       "      <td>0.711815</td>\n",
+       "      <td>[[488, 58], [252, 309]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.768403</td>\n",
+       "      <td>0.765131</td>\n",
+       "      <td>0.765131</td>\n",
+       "      <td>0.764652</td>\n",
+       "      <td>[[445, 101], [159, 402]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.919226</td>\n",
+       "      <td>0.916893</td>\n",
+       "      <td>0.916893</td>\n",
+       "      <td>0.916730</td>\n",
+       "      <td>[[479, 67], [25, 536]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.960628</td>\n",
+       "      <td>0.957543</td>\n",
+       "      <td>0.957543</td>\n",
+       "      <td>0.957496</td>\n",
+       "      <td>[[545, 1], [46, 515]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.770355</td>\n",
+       "      <td>0.768744</td>\n",
+       "      <td>0.768744</td>\n",
+       "      <td>0.768213</td>\n",
+       "      <td>[[395, 151], [105, 456]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.854529</td>\n",
+       "      <td>0.841915</td>\n",
+       "      <td>0.841915</td>\n",
+       "      <td>0.840738</td>\n",
+       "      <td>[[510, 36], [139, 422]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model  Precision Score  Accuracy Score  Recall Score  F1 Score  \\\n",
+       "0    LR         0.973314        0.971996      0.971996  0.971987   \n",
+       "1   SVC         0.751947        0.719964      0.719964  0.711815   \n",
+       "2   KNN         0.768403        0.765131      0.765131  0.764652   \n",
+       "3   DTC         0.919226        0.916893      0.916893  0.916730   \n",
+       "4  SGDC         0.960628        0.957543      0.957543  0.957496   \n",
+       "5    NB         0.770355        0.768744      0.768744  0.768213   \n",
+       "6    RF         0.854529        0.841915      0.841915  0.840738   \n",
+       "\n",
+       "           Confusion Matrix  \n",
+       "0     [[545, 1], [30, 531]]  \n",
+       "1   [[488, 58], [252, 309]]  \n",
+       "2  [[445, 101], [159, 402]]  \n",
+       "3    [[479, 67], [25, 536]]  \n",
+       "4     [[545, 1], [46, 515]]  \n",
+       "5  [[395, 151], [105, 456]]  \n",
+       "6   [[510, 36], [139, 422]]  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in sklearn_models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=\"weighted\")\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=\"weighted\")\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=\"weighted\")\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[1, 0])\n",
+    "    testset_final_results.append(( name,  p_score, a_score, r_score, f1_score_result, cm ))\n",
+    "\n",
+    "\n",
+    "sklearn_eval = pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy Score\", \"Recall Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "sklearn_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Deep learning models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-27 13:05:38.881823: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
+      "2022-11-27 13:05:38.941570: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-27 13:05:39.153747: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-27 13:05:39.316420: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-27 13:05:39.450061: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.892070</td>\n",
+       "      <td>0.864499</td>\n",
+       "      <td>0.864499</td>\n",
+       "      <td>0.862354</td>\n",
+       "      <td>[[544, 2], [148, 413]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.860509</td>\n",
+       "      <td>0.831075</td>\n",
+       "      <td>0.831075</td>\n",
+       "      <td>0.827950</td>\n",
+       "      <td>[[531, 15], [172, 389]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.936507</td>\n",
+       "      <td>0.927733</td>\n",
+       "      <td>0.927733</td>\n",
+       "      <td>0.927442</td>\n",
+       "      <td>[[545, 1], [79, 482]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.837910</td>\n",
+       "      <td>0.773261</td>\n",
+       "      <td>0.773261</td>\n",
+       "      <td>0.762745</td>\n",
+       "      <td>[[541, 5], [246, 315]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model  Precision Score  Accuracy Score  Recall Score  \\\n",
+       "0  7_layers_with_dropout         0.892070        0.864499      0.864499   \n",
+       "1               5_layers         0.860509        0.831075      0.831075   \n",
+       "2               3_layers         0.936507        0.927733      0.927733   \n",
+       "3               7_layers         0.837910        0.773261      0.773261   \n",
+       "\n",
+       "   F1 Score         Confusion Matrix  \n",
+       "0  0.862354   [[544, 2], [148, 413]]  \n",
+       "1  0.827950  [[531, 15], [172, 389]]  \n",
+       "2  0.927442    [[545, 1], [79, 482]]  \n",
+       "3  0.762745   [[541, 5], [246, 315]]  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in dp_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[1, 0])\n",
+    "    p_score = precision_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    a_score = accuracy_score(y_test_class, y_pred_class)\n",
+    "    r_score = recall_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    f1_score_result = f1_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    \n",
+    "    test_set_results.append(( name,  (p_score), a_score, (r_score), (f1_score_result), cm ))\n",
+    "\n",
+    "dp_eval = pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Accuracy Score\", \"Recall Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "dp_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Final Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.973314</td>\n",
+       "      <td>0.971996</td>\n",
+       "      <td>0.971996</td>\n",
+       "      <td>0.971987</td>\n",
+       "      <td>[[545, 1], [30, 531]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.960628</td>\n",
+       "      <td>0.957543</td>\n",
+       "      <td>0.957543</td>\n",
+       "      <td>0.957496</td>\n",
+       "      <td>[[545, 1], [46, 515]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.936507</td>\n",
+       "      <td>0.927733</td>\n",
+       "      <td>0.927733</td>\n",
+       "      <td>0.927442</td>\n",
+       "      <td>[[545, 1], [79, 482]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.919226</td>\n",
+       "      <td>0.916893</td>\n",
+       "      <td>0.916893</td>\n",
+       "      <td>0.916730</td>\n",
+       "      <td>[[479, 67], [25, 536]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.892070</td>\n",
+       "      <td>0.864499</td>\n",
+       "      <td>0.864499</td>\n",
+       "      <td>0.862354</td>\n",
+       "      <td>[[544, 2], [148, 413]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.854529</td>\n",
+       "      <td>0.841915</td>\n",
+       "      <td>0.841915</td>\n",
+       "      <td>0.840738</td>\n",
+       "      <td>[[510, 36], [139, 422]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.860509</td>\n",
+       "      <td>0.831075</td>\n",
+       "      <td>0.831075</td>\n",
+       "      <td>0.827950</td>\n",
+       "      <td>[[531, 15], [172, 389]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.770355</td>\n",
+       "      <td>0.768744</td>\n",
+       "      <td>0.768744</td>\n",
+       "      <td>0.768213</td>\n",
+       "      <td>[[395, 151], [105, 456]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.768403</td>\n",
+       "      <td>0.765131</td>\n",
+       "      <td>0.765131</td>\n",
+       "      <td>0.764652</td>\n",
+       "      <td>[[445, 101], [159, 402]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.837910</td>\n",
+       "      <td>0.773261</td>\n",
+       "      <td>0.773261</td>\n",
+       "      <td>0.762745</td>\n",
+       "      <td>[[541, 5], [246, 315]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.751947</td>\n",
+       "      <td>0.719964</td>\n",
+       "      <td>0.719964</td>\n",
+       "      <td>0.711815</td>\n",
+       "      <td>[[488, 58], [252, 309]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Model  Precision Score  Accuracy Score  Recall Score  \\\n",
+       "0                      LR         0.973314        0.971996      0.971996   \n",
+       "1                    SGDC         0.960628        0.957543      0.957543   \n",
+       "2                3_layers         0.936507        0.927733      0.927733   \n",
+       "3                     DTC         0.919226        0.916893      0.916893   \n",
+       "4   7_layers_with_dropout         0.892070        0.864499      0.864499   \n",
+       "5                      RF         0.854529        0.841915      0.841915   \n",
+       "6                5_layers         0.860509        0.831075      0.831075   \n",
+       "7                      NB         0.770355        0.768744      0.768744   \n",
+       "8                     KNN         0.768403        0.765131      0.765131   \n",
+       "9                7_layers         0.837910        0.773261      0.773261   \n",
+       "10                    SVC         0.751947        0.719964      0.719964   \n",
+       "\n",
+       "    F1 Score          Confusion Matrix  \n",
+       "0   0.971987     [[545, 1], [30, 531]]  \n",
+       "1   0.957496     [[545, 1], [46, 515]]  \n",
+       "2   0.927442     [[545, 1], [79, 482]]  \n",
+       "3   0.916730    [[479, 67], [25, 536]]  \n",
+       "4   0.862354    [[544, 2], [148, 413]]  \n",
+       "5   0.840738   [[510, 36], [139, 422]]  \n",
+       "6   0.827950   [[531, 15], [172, 389]]  \n",
+       "7   0.768213  [[395, 151], [105, 456]]  \n",
+       "8   0.764652  [[445, 101], [159, 402]]  \n",
+       "9   0.762745    [[541, 5], [246, 315]]  \n",
+       "10  0.711815   [[488, 58], [252, 309]]  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_df = pd.concat([sklearn_eval, dp_eval])\n",
+    "eval_df = eval_df.sort_values(by=['F1 Score'], ascending=False).reset_index(drop=True)\n",
+    "eval_df.to_csv(f\"err.evaluation.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "eval_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Best model - ROC - Confusion Matrix\n",
+    "\n",
+    "As we can see from the evaluation, the best model according to the F1 Score is the LR model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.9981203 , 0.94782609]),\n",
+       " array([0.94652406, 0.9981685 ]),\n",
+       " array([0.97163769, 0.97234612]))"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model = sklearn_models[\"LR\"]\n",
+    "y_predictions = best_model.predict(test_x)\n",
+    "\n",
+    "p_score = precision_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "r_score = recall_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "f1_score_result = f1_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "\n",
+    "p_score, r_score, f1_score_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9724999999999999"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0.998 + 0.947) / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GjMmYMWOyaNGiDBgwIDNmzEiSNDU15fzzz8/cuXMzfPjwDBs2LPPmzcu4ceMKD2O8HI0dAEAPuvTSS7Nr167MnDmzvkHxypUr63vYJcnVV1+dvn37Zvr06fUNipcuXdqtPeySV7FBcU+xQTFUiw2Kobp6e4PiUR+5osfu/fjf/c8eu3dPssYOAKAiTMUCAOV0EKyxO9hI7AAAKkJjBwBQERo7AICKsMYOACinbn7d1muBxg4AKCd9XYGpWACAitDYAQBUhMYOAKAirLEDAMrJGrsCiR0AQEVI7ACAcpLYFUjsAAAqQmMHAFARpmIBgHIyFVsgsQMAqAiJHQBQSg2+K7ZAYgcAUBEaOwCAitDYAQBUhDV2AEA5WWJXILEDAKgIiR0AUE4SuwKJHQBARWjsAAAqwlQsAFBK9icuktgBAFSExg4AoCI0dgAAFWGNHQBQTtbYFUjsAAAqQmIHAJSTxK5AYgcAUBESOwCglAR2RRo7AKCc7FBcYCoWAKAiJHYAQCkJ7IokdgAAFaGxAwCoCI0dAEBFWGMHAJSTNXYFEjsAgIqQ2AEApSSwK9LYAQDlpLMrMBULAFAREjsAoJRsUFwksQMAqAiNHQBARWjsAAAqwho7AKCUrLErktgBAFSExg4AoCJMxQIApWQqtkhiBwBQERI7AKCcJHYFEjsAgIqQ2AEApdQgsiuQ2AEAVITEDgAoJ4FdgcQOAKAiJHYAQCkJ7Io0dgBAKdmguMhULABARUjsAIByktgVSOwAACpCYgcAlJLArkhiBwBQERI7AKCcRHYFEjsAgIqQ2AEApSSwK9LYAQClZIPiIlOxAAAVIbEDAMpJYlcgsQMAeBWWLFmS448/PoMHD87gwYNzyimn5Fvf+lb9eq1Wy8KFCzNy5MgceuihOe2003Lfffd1uEd7e3tmzZqVESNGZODAgTnrrLOycePGbteisQMASqmhB4/uOPLII/PXf/3Xueuuu3LXXXfl3e9+d973vvfVm7crr7wyV111Va655pqsXbs2LS0tOf3007N9+/b6PWbPnp1bbrklK1asyJo1a7Jjx45MmzYte/fu7VYtpmIBADppb29Pe3t7h3ONjY1pbGwsjD3zzDM7vP/MZz6TJUuW5I477shb3vKWLF68OAsWLMg555yTJFm2bFmam5uzfPnyXHDBBdm6dWtuuOGG3HTTTZk8eXKS5Oabb86oUaNy22235Ywzzuhy3RI7AKCUGhp67mhtbU1TU1OHo7W19WVr2rt3b1asWJGdO3fmlFNOySOPPJK2trZMmTKlPqaxsTGnnnpqbr/99iTJunXrsmfPng5jRo4cmbFjx9bHdJXEDgCgk/nz52fOnDkdzr1UWvdr69evzymnnJLnn38+hx12WG655Za85S1vqTdmzc3NHcY3Nzfn0UcfTZK0tbWlf//+GTp0aGFMW1tbt+rW2AEAdLKvadd9edOb3pR77rknzz77bP71X/815513XlavXl2/3tBp071arVY411lXxnRmKhYAKKWenIrtrv79++eYY47JiSeemNbW1rz1rW/NF77whbS0tCRJIXnbvHlzPcVraWnJ7t27s2XLln2O6SqNHQDAflar1dLe3p7Ro0enpaUlq1atql/bvXt3Vq9enYkTJyZJxo8fn379+nUYs2nTpmzYsKE+pqtMxQIA5XSQbFD8iU98IlOnTs2oUaOyffv2rFixIt/73vdy6623pqGhIbNnz86iRYsyZsyYjBkzJosWLcqAAQMyY8aMJElTU1POP//8zJ07N8OHD8+wYcMyb968jBs3rv6UbFdp7AAAXoUnnngiH/zgB7Np06Y0NTXl+OOPz6233prTTz89SXLppZdm165dmTlzZrZs2ZIJEyZk5cqVGTRoUP0eV199dfr27Zvp06dn165dmTRpUpYuXZo+ffp0q5aGWq1W26+/3as07oS5vV0CsB+tv/vz9dfL7/5CL1YC7G8zTrikV3/+Wz91VY/d+yeXzXn5QQcha+wAACrCVCwAUEqv5OnVqpPYAQBUhMYOAKAiTMUCAKVkKrZIYgcAUBESOwCglAR2RRI7AICKkNgBAOUksiuQ2AEAVITEDgAoJU/FFknsAAAqQmIHAJSSwK5IYwcAlJPOruCga+zW3/353i4B6CEzTrikt0sAqLSDrrEDAOgKgV2RhycAACrioEvslvzoi71dArAfXXjKx+uvx530F71YCbC/rb/zs7368213UiSxAwCoiIMusQMA6BKJXYHEDgCgIiR2AEApCeyKNHYAQCl5eKLIVCwAQEVI7ACAkhLZdSaxAwCoCIkdAFBK1tgVSewAACpCYgcAlJPErkBiBwBQERI7AKCUBHZFEjsAgIqQ2AEApeSp2CKJHQBARWjsAAAqwlQsAFBKpmKLJHYAABUhsQMASklgVySxAwCoCIkdAFBOIrsCiR0AQEVI7ACAUvJUbJHGDgAoJX1dkalYAICKkNgBAOUksiuQ2AEAVITEDgAoJYFdkcQOAKAiJHYAQCnZ7qRIYgcAUBESOwCgnER2BRo7AKCUtHVFpmIBACpCYgcAlJPIrkBiBwBQERI7AKCUBHZFEjsAgIqQ2AEApWS3kyKJHQBARUjsAIByktgVaOwAgFLS1xWZigUAqAiJHQBQSh6eKJLYAQBUhMYOAKAiNHYAABVhjR0AUErW2BVJ7AAAKkJiBwCUksSuSGIHAFARGjsAgIowFQsAlJKp2CKJHQBARWjsAIBSaujBoztaW1vz9re/PYMGDcrhhx+es88+Ow8++GCHMbVaLQsXLszIkSNz6KGH5rTTTst9993XYUx7e3tmzZqVESNGZODAgTnrrLOycePGbtWisQMAeBVWr16diy66KHfccUdWrVqVF154IVOmTMnOnTvrY6688spcddVVueaaa7J27dq0tLTk9NNPz/bt2+tjZs+enVtuuSUrVqzImjVrsmPHjkybNi179+7tci3W2AEA5XSQrLG79dZbO7y/8cYbc/jhh2fdunV55zvfmVqtlsWLF2fBggU555xzkiTLli1Lc3Nzli9fngsuuCBbt27NDTfckJtuuimTJ09Oktx8880ZNWpUbrvttpxxxhldqkViBwDQSXt7e7Zt29bhaG9v79Jnt27dmiQZNmxYkuSRRx5JW1tbpkyZUh/T2NiYU089NbfffnuSZN26ddmzZ0+HMSNHjszYsWPrY7pCYwcAlFJDQ88dra2taWpq6nC0tra+bE21Wi1z5szJ7//+72fs2LFJkra2tiRJc3Nzh7HNzc31a21tbenfv3+GDh26zzFdYSoWACilnpyJnT9/fubMmdPhXGNj48t+7uKLL869996bNWvWFK41dNqfpVarFc511pUxv0liBwDQSWNjYwYPHtzheLnGbtasWfna176Wf//3f8+RRx5ZP9/S0pIkheRt8+bN9RSvpaUlu3fvzpYtW/Y5pis0dgBAOfXkXGw31Gq1XHzxxfnyl7+c7373uxk9enSH66NHj05LS0tWrVpVP7d79+6sXr06EydOTJKMHz8+/fr16zBm06ZN2bBhQ31MV5iKBQB4FS666KIsX748X/3qVzNo0KB6MtfU1JRDDz00DQ0NmT17dhYtWpQxY8ZkzJgxWbRoUQYMGJAZM2bUx55//vmZO3duhg8fnmHDhmXevHkZN25c/SnZrtDYAQCldJDsdpIlS5YkSU477bQO52+88cZ86EMfSpJceuml2bVrV2bOnJktW7ZkwoQJWblyZQYNGlQff/XVV6dv376ZPn16du3alUmTJmXp0qXp06dPl2vR2AEAvAq1Wu1lxzQ0NGThwoVZuHDhPscccsgh+dKXvpQvfelLr7gWjR0AUErdXAr3muDhCQCAipDYAQClJLErktgBAFSExg4AoCJMxQIApWQqtkhiBwBQERI7AKCUBHZFEjsAgIqQ2AEA5SSyK5DYAQBUhMQOACglgV2Rxg4AKCXbnRSZigUAqAiJHQBQShK7IokdAEBFaOwAACpCYwcAUBHW2AEApWSNXZHEDgCgIiR2AEApCeyKJHYAABUhsQMASskauyKNHQBQShq7IlOxAAAVobEDAKgIjR0AQEVYYwcAlJI1dkUSOwCAipDYAQClJLArktgBAFSExA4AKCVr7Io0dgBAKenrikzFAgBUhMQOACgnkV2BxA4AoCIkdgBAKXl4okhiBwBQERI7AKCUBHZFEjsAgIqQ2AEApWSNXZHGjh7xk+9uyPrvbsi2p7YlSYa9flgmvO/tGX38G5IktVotd3xlbTasvi/P72xPy9HNeff/9c4Mf/3w3iwb6OTCj56emR+d0uHcU09vz7umfrp+ferpv5vm5iF5Yc8L+ekDv8gXl3wr6+97vD7+/WdPyHvOeFt+502vz2GHHZKJ7/7LbN/x/AH9PagmfV2Rxo4eMWjowPzeH52cIc1DkiQ/XfNAvv6Fb+bcT0/P8NcPz13f/HF+/O17MuUjkzKkZUju/Npd+fJnv5bzWs9N/0P7927xQAcP/awtH734+vr7F/e+WH/96GNPZtFnv5KNv3g6jYf0ywc/8I78zZc+mveec0W2PLszSXLIIf3ywx89mB/+6MHMvvg9B7x+eC3R2NEjjn7b6A7vf+/9J+fef9+QTQ8/kWEjh+XHK3+St595Yo458Y1JkikfnZzrP/73eeCO/53j3zW2N0oG9mHv3hfz9NPbX/LaN799T4f3n1389fzh+ybk2DFH5D/WPpwkuXnFmiTJiScc3aN18tpjKrZIY0ePe/HFF/PQnT/LC+17csQxLdn25LY8t/W5vGHsqPqYvv365Mg3j8ymh9s0dnCQOWrUiHznG5/M7j17s37DY/nidd/Kxl8+UxjXt2+fvP/sk7Nt+648+L9/2QuVAvu1sXv88cdz2WWX5e///u/3520pqacefzr/fPm/5IU9e9OvsV+mzZqa4a8fll8+tClJMmDwgA7jBwwekG37SAWA3rF+w2NZsHBFHn3syQwfNigf+/Ck3HTDxTn7Tz6XrVufS5K88/d/J5+9/Nwccki/PPnU9nzs4uvz7H9dg54ksCvar9udPPPMM1m2bNnLjmtvb8+2bds6HO3t7fuzFA4CQ48YknM//cf5k798f45/99is/Lvv5Olf/J//yy9E6LWkwZ8pHFTW/OjB3Pbv6/PQz9pyx9qHctGf35Aked97T6yPWXvXw3n/n16dD37k2vzwjgfzudYPZtjQgb1VMrym9co+dq2trWlqaupwtLa29kYp9KA+fftkSPOQNI8+PL//R6dkxKgR+fGqn2Rg06+Sup2d/o/+ue3PZUDTob1RKtBFu57fk4ce3pSjRo3ocO7xjU/n3g2P5bLL/7/sfWFv/uCsk3qxSl4rGhp67iirXmns5s+fn61bt3Y45s+f3xulcCDVatm758UMft3gDGgakMd+YzuEvS/szcYHfpkjjmnpxQKBl9OvX58c/duH56n/2sropTQ0NKR/f0u4oTf0yl9eY2NjGhsbe+NHc4D88F9+lN8e94YcNuyw7Hl+Tx78j4ey8YFf5uy5Z6ahoSFvm/LW3Pn1dRnSPCRDmpuy9t/WpV9j37z55GN7u3TgN8z9+LSs/sFPs+mJLRk29LB87MOTM3DgIfnqN+7KoYf0y0f/bFK+94Of5smntmVI08D88ftPSfPhTVn5nXvr9xg+fFBGDBtUT/nGHHNEdu5sz6YntmTbtl299atRBSVO1npKtxq7c84557+9/uyzz76aWqiQ57buyq3X35bntu5M/0MbM2LU8Jw998z6k7AnvudteWH3C/nuP6xO+872tLyxOX8w7yx72MFBpvnwplxx+YwMHTIwz2zZmXs3PJZzz/9SNrU9m/79+2b0bx+es957YoYOGZhnt+7MfT/dmPM+dl1+9vMn6veYfs7JHTY5Xnb9zCTJJz/1z/nqN+464L8T1aGvK2qo1Wq1rg7+sz/7sy6Nu/HGG19xQUt+9MVX/Fng4HPhKR+vvx530l/0YiXA/rb+zs/26s//yL9+ocfu/Xd/eEmP3bsndSuxezUNGwDA/lTmhxx6Sq88PAEAwP7nsSUAoJQEdkUSOwCAipDYAQClZI1dkcQOAKAiJHYAQCkJ7Io0dgBAKZmKLTIVCwBQERI7AKCUJHZFEjsAgIqQ2AEApSSwK5LYAQBUhMQOACgla+yKJHYAABUhsQMASklgV6SxAwBKyVRskalYAICKkNgBAKUksCuS2AEAVITEDgAoJWvsiiR2AAAVobEDAEqpoQeP7vj+97+fM888MyNHjkxDQ0O+8pWvdLheq9WycOHCjBw5MoceemhOO+203HfffR3GtLe3Z9asWRkxYkQGDhyYs846Kxs3buxmJRo7AIBXZefOnXnrW9+aa6655iWvX3nllbnqqqtyzTXXZO3atWlpacnpp5+e7du318fMnj07t9xyS1asWJE1a9Zkx44dmTZtWvbu3dutWqyxAwBK6WBZYzd16tRMnTr1Ja/VarUsXrw4CxYsyDnnnJMkWbZsWZqbm7N8+fJccMEF2bp1a2644YbcdNNNmTx5cpLk5ptvzqhRo3LbbbfljDPO6HItEjsAoJR6ciq2vb0927Zt63C0t7d3u8ZHHnkkbW1tmTJlSv1cY2NjTj311Nx+++1JknXr1mXPnj0dxowcOTJjx46tj+kqjR0AQCetra1pamrqcLS2tnb7Pm1tbUmS5ubmDuebm5vr19ra2tK/f/8MHTp0n2O6ylQsAFBKPTkVO3/+/MyZM6fDucbGxld8v4ZOxdZqtcK5zroypjOJHQBAJ42NjRk8eHCH45U0di0tLUlSSN42b95cT/FaWlqye/fubNmyZZ9jukpjBwCUUkNDzx37y+jRo9PS0pJVq1bVz+3evTurV6/OxIkTkyTjx49Pv379OozZtGlTNmzYUB/TVaZiAQBehR07duThhx+uv3/kkUdyzz33ZNiwYTnqqKMye/bsLFq0KGPGjMmYMWOyaNGiDBgwIDNmzEiSNDU15fzzz8/cuXMzfPjwDBs2LPPmzcu4cePqT8l2lcYOACilg2S3k9x1111517veVX//67V55513XpYuXZpLL700u3btysyZM7Nly5ZMmDAhK1euzKBBg+qfufrqq9O3b99Mnz49u3btyqRJk7J06dL06dOnW7U01Gq12v75tfaPJT/6Ym+XAOxHF57y8frrcSf9RS9WAuxv6+/8bK/+/Eu/9YUeu/eVUy/psXv3JIkdAFBK3X1i9LVAYwcAlJK2rshTsQAAFSGxAwBKyUxskcQOAKAiJHYAQCkJ7IokdgAAFSGxAwBK6bdEdgUSOwCAipDYAQClJLArktgBAFSExA4AKCX72BVp7ACAUtLXFZmKBQCoCIkdAFBKpmKLJHYAABUhsQMASklgVySxAwCoCIkdAFBK1tgVSewAACpCYgcAlJLArkhjBwCU0m/p7ApMxQIAVITEDgAoJYFdkcQOAKAiJHYAQCnZ7qRIYgcAUBESOwCglAR2RRI7AICKkNgBAKVkjV2Rxg4AKCV9XZGpWACAipDYAQClZCq2SGIHAFAREjsAoJQkdkUSOwCAipDYAQClJJ0q8m8CAFAREjsAoJSssSvS2AEApaSvKzIVCwBQERI7AKCUTMUWSewAACpCYgcAlJLArkhiBwBQERI7AKCUrLErktgBAFSExA4AKCWBXZHGDgAoJVOxRaZiAQAqQmIHAJSSwK5IYgcAUBESOwCglKyxK5LYAQBUhMQOACgl6VSRfxMAgIqQ2AEApWSNXZHGDgAoJX1dkalYAICKkNgBAKVkKrZIYgcAUBESOwCglAR2RRI7AICKkNgBAKVkjV2RxA4AoCIkdgBAKUnsijR2AEAp6euKTMUCAFSExA4AKCVTsUUSOwCAipDYAQClJJ0q8m8CAFAREjsAoJSssSuS2AEAVITEDgAopYbUeruEg47GDgAoJVOxRQddY3fhKR/v7RKAHrL+zs/2dgkAldZQq9XkmBxQ7e3taW1tzfz589PY2Njb5QD7kb9v6F0aOw64bdu2pampKVu3bs3gwYN7uxxgP/L3Db3LU7EAABWhsQMAqAiNHQBARWjsOOAaGxtz2WWXWVgNFeTvG3qXhycAACpCYgcAUBEaOwCAitDYAQBUhMYOAKAiNHYAABWhseOAamtry6xZs3L00UensbExo0aNyplnnpnvfOc7vV0a8Cp86EMfytlnn93bZcBrXt/eLoDXjv/8z//M7/3e72XIkCG58sorc/zxx2fPnj359re/nYsuuigPPPBAb5cIAKWmseOAmTlzZhoaGnLnnXdm4MCB9fPHHXdcPvzhD/diZQBQDaZiOSCeeeaZ3Hrrrbnooos6NHW/NmTIkANfFABUjMaOA+Lhhx9OrVbLm9/85t4uBQAqS2PHAfHrb65raGjo5UoAoLo0dhwQY8aMSUNDQ+6///7eLgUAKktjxwExbNiwnHHGGbn22muzc+fOwvVnn332wBcFABXjqVgOmOuuuy4TJ07MSSedlE9/+tM5/vjj88ILL2TVqlVZsmSJNA9KbuvWrbnnnns6nBs2bFiOOuqo3ikIXoM0dhwwo0ePzt13353PfOYzmTt3bjZt2pTXve51GT9+fJYsWdLb5QGv0ve+97287W1v63DuvPPOy9KlS3unIHgNaqj9elU7AAClZo0dAEBFaOwAACpCYwcAUBEaOwCAitDYAQBUhMYOAKAiNHYAABWhsQMAqAiNHQBARWjsAAAqQmMHAFAR/z+KakXwNNLhoAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "KNN_cm = eval_df[ eval_df[\"Model\"] == 'LR' ][\"Confusion Matrix\"].values[0]\n",
+    "\n",
+    "cm_array_df = pd.DataFrame(KNN_cm, index=[\"C\", \"L\"], columns=[\"C\", \"L\"])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6)) \n",
+    "sns.heatmap(cm_array_df, linewidths=1, annot=True, ax=ax, fmt='g', cmap=\"crest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. F1 Score and Confidence correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_labels(y_pred, y_pred_proba, threshold):\n",
+    "    '''Return prediction taking confidence threshold into account'''\n",
+    "    results = []\n",
+    "\n",
+    "    for index, predicted_class in enumerate(y_pred):\n",
+    "        prediction_probabilities = y_pred_proba[index]\n",
+    "        class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
+    "\n",
+    "        results.append(predicted_class if class_prediction_probability >= threshold else -1)\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def calculate_correlation_score_confidence(test_x, test_y):\n",
+    "    '''Calculate correlation between Precision score/Recall score/F1 score and confidence threshold'''\n",
+    "    y_predictions = best_model.predict(test_x)\n",
+    "    y_predict_proba = best_model.predict_proba(test_x)\n",
+    "\n",
+    "    thresholds = list(np.arange(0, 1.05, 0.01))\n",
+    "\n",
+    "    f1_score_results = []\n",
+    "\n",
+    "    for threshold in thresholds:\n",
+    "        true_predictions = to_labels(y_predictions, y_predict_proba, threshold)\n",
+    "        f1_s = list(f1_score(test_y, true_predictions, labels=[1, 0], average=None))\n",
+    "        all_class_f1 = f1_score(test_y, true_predictions, labels=[0, 1, 2], average=\"weighted\")\n",
+    "        f1_s.append(all_class_f1)\n",
+    "        f1_score_results.append(f1_s)\n",
+    "    \n",
+    "    return thresholds, f1_score_results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thresholds, f1_scores = calculate_correlation_score_confidence(test_x, test_y)\n",
+    "\n",
+    "first_class = [ el[0] for el in f1_scores ]\n",
+    "second_class = [ el[1] for el in f1_scores ]\n",
+    "all_classes = [ el[2] for el in f1_scores ]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "plt.plot(thresholds, first_class, label = \"F1 Score - Correct class\")\n",
+    "plt.plot(thresholds, second_class, label = \"F1 Score - Incorrect class\")\n",
+    "plt.plot(thresholds, all_classes, label = \"F1 Score - All classes\", linewidth=2.0, color=\"blue\")\n",
+    "plt.legend(loc = 'lower left')\n",
+    "plt.ylim([0.6, 1])\n",
+    "plt.xlim([0.025, 1])\n",
+    "plt.xlabel(\"Threshold\", fontsize = 12)\n",
+    "plt.ylabel(\"F1 Score\", fontsize = 12)\n",
+    "# plt.axvline(thresholds[np.argmin(abs(precision-recall))], color=\"k\", ls = \"--\")\n",
+    "# plt.title(label = F\"Threshold = {thresholds[np.argmin(abs(precision-recall))]:.3f}\", fontsize = 12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal Threshold: 0.6863927762156125\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# calculate the fpr and tpr for all thresholds of the classification\n",
+    "probs = best_model.predict_proba(test_x)\n",
+    "preds = probs[:,1]\n",
+    "fpr, tpr, threshold = roc_curve(test_y, preds)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "optimal_idx = np.argmax(tpr - fpr)\n",
+    "optimal_threshold = threshold[optimal_idx]\n",
+    "print(f\"Optimal Threshold: {optimal_threshold}\")\n",
+    "\n",
+    "# method I: plt\n",
+    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
+    "plt.plot([0, 1], [0, 1],'r--', label=\"Random Guess\")\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/9.err.detection.ipynb b/core/lunge_model/9.err.detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b8f87f5b065904df0506ba39367a14bf38d0b112
--- /dev/null
+++ b/core/lunge_model/9.err.detection.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[21249]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x111188860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece480). One of the two will be used. Which one is undefined.\n",
+      "objc[21249]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107550a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[21249]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107550a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[21249]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107550ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15eece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import traceback\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup important landmarks and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for lunge\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_important_keypoints(results) -> list:\n",
+    "    '''\n",
+    "    Extract important keypoints from mediapipe pose detection\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "    data = []\n",
+    "    for lm in IMPORTANT_LMS:\n",
+    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "    \n",
+    "    return np.array(data).flatten().tolist()\n",
+    "\n",
+    "\n",
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
+    "    '''\n",
+    "    Calculate the angle between 3 points\n",
+    "    Unit of the angle will be in Degree\n",
+    "    '''\n",
+    "    point1 = np.array(point1)\n",
+    "    point2 = np.array(point2)\n",
+    "    point3 = np.array(point3)\n",
+    "\n",
+    "    # Calculate algo\n",
+    "    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
+    "    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
+    "\n",
+    "    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
+    "    return angleInDeg\n",
+    "    \n",
+    "\n",
+    "def analyze_knee_angle(\n",
+    "    mp_results, stage: str, angle_thresholds: list, draw_to_image: tuple = None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate angle of each knee while performer at the DOWN position\n",
+    "\n",
+    "    Return result explanation:\n",
+    "        error: True if at least 1 error\n",
+    "        right\n",
+    "            error: True if an error is on the right knee\n",
+    "            angle: Right knee angle\n",
+    "        left\n",
+    "            error: True if an error is on the left knee\n",
+    "            angle: Left knee angle\n",
+    "    \"\"\"\n",
+    "    results = {\n",
+    "        \"error\": None,\n",
+    "        \"right\": {\"error\": None, \"angle\": None},\n",
+    "        \"left\": {\"error\": None, \"angle\": None},\n",
+    "    }\n",
+    "\n",
+    "    landmarks = mp_results.pose_landmarks.landmark\n",
+    "\n",
+    "    # Calculate right knee angle\n",
+    "    right_hip = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y,\n",
+    "    ]\n",
+    "    right_knee = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y,\n",
+    "    ]\n",
+    "    right_ankle = [\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y,\n",
+    "    ]\n",
+    "    results[\"right\"][\"angle\"] = calculate_angle(right_hip, right_knee, right_ankle)\n",
+    "\n",
+    "    # Calculate left knee angle\n",
+    "    left_hip = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y,\n",
+    "    ]\n",
+    "    left_knee = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y,\n",
+    "    ]\n",
+    "    left_ankle = [\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,\n",
+    "        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y,\n",
+    "    ]\n",
+    "    results[\"left\"][\"angle\"] = calculate_angle(left_hip, left_knee, left_ankle)\n",
+    "\n",
+    "    # Draw to image\n",
+    "    if draw_to_image is not None and stage != \"down\":\n",
+    "        (image, video_dimensions) = draw_to_image\n",
+    "\n",
+    "        # Visualize angles\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"right\"][\"angle\"])),\n",
+    "            tuple(np.multiply(right_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            0.5,\n",
+    "            (255, 255, 255),\n",
+    "            1,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"left\"][\"angle\"])),\n",
+    "            tuple(np.multiply(left_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            0.5,\n",
+    "            (255, 255, 255),\n",
+    "            1,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "\n",
+    "    if stage != \"down\":\n",
+    "        return results\n",
+    "\n",
+    "    # Evaluation\n",
+    "    results[\"error\"] = False\n",
+    "\n",
+    "    if angle_thresholds[0] <= results[\"right\"][\"angle\"] <= angle_thresholds[1]:\n",
+    "        results[\"right\"][\"error\"] = False\n",
+    "    else:\n",
+    "        results[\"right\"][\"error\"] = True\n",
+    "        results[\"error\"] = True\n",
+    "\n",
+    "    if angle_thresholds[0] <= results[\"left\"][\"angle\"] <= angle_thresholds[1]:\n",
+    "        results[\"left\"][\"error\"] = False\n",
+    "    else:\n",
+    "        results[\"left\"][\"error\"] = True\n",
+    "        results[\"error\"] = True\n",
+    "\n",
+    "    # Draw to image\n",
+    "    if draw_to_image is not None:\n",
+    "        (image, video_dimensions) = draw_to_image\n",
+    "\n",
+    "        right_color = (255, 255, 255) if not results[\"right\"][\"error\"] else (0, 0, 255)\n",
+    "        left_color = (255, 255, 255) if not results[\"left\"][\"error\"] else (0, 0, 255)\n",
+    "\n",
+    "        right_font_scale = 0.5 if not results[\"right\"][\"error\"] else 1\n",
+    "        left_font_scale = 0.5 if not results[\"left\"][\"error\"] else 1\n",
+    "\n",
+    "        right_thickness = 1 if not results[\"right\"][\"error\"] else 2\n",
+    "        left_thickness = 1 if not results[\"left\"][\"error\"] else 2\n",
+    "\n",
+    "        # Visualize angles\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"right\"][\"angle\"])),\n",
+    "            tuple(np.multiply(right_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            right_font_scale,\n",
+    "            right_color,\n",
+    "            right_thickness,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "        cv2.putText(\n",
+    "            image,\n",
+    "            str(int(results[\"left\"][\"angle\"])),\n",
+    "            tuple(np.multiply(left_knee, video_dimensions).astype(int)),\n",
+    "            cv2.FONT_HERSHEY_COMPLEX,\n",
+    "            left_font_scale,\n",
+    "            left_color,\n",
+    "            left_thickness,\n",
+    "            cv2.LINE_AA,\n",
+    "        )\n",
+    "\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VIDEO_PATH1 = \"../data/lunge/lunge_test_3.mp4\"\n",
+    "VIDEO_PATH2 = \"../data/lunge/lunge_test_5.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    input_scaler = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Detection with Sklearn Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/sklearn/stage_SVC_model.pkl\", \"rb\") as f:\n",
+    "    stage_sklearn_model = pickle.load(f)\n",
+    "\n",
+    "with open(\"./model/sklearn/err_LR_model.pkl\", \"rb\") as f:\n",
+    "    err_sklearn_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n"
+     ]
+    }
+   ],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH1)\n",
+    "current_stage = \"\"\n",
+    "counter = 0\n",
+    "\n",
+    "prediction_probability_threshold = 0.8\n",
+    "ANGLE_THRESHOLDS = [60, 135]\n",
+    "\n",
+    "knee_over_toe = False\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            stage_predicted_class = stage_sklearn_model.predict(X)[0]\n",
+    "            stage_prediction_probabilities = stage_sklearn_model.predict_proba(X)[0]\n",
+    "            stage_prediction_probability = round(stage_prediction_probabilities[stage_prediction_probabilities.argmax()], 2)\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if stage_predicted_class == \"I\" and stage_prediction_probability >= prediction_probability_threshold:\n",
+    "                current_stage = \"init\"\n",
+    "            elif stage_predicted_class == \"M\" and stage_prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"mid\"\n",
+    "            elif stage_predicted_class == \"D\" and stage_prediction_probability >= prediction_probability_threshold:\n",
+    "                if current_stage in [\"mid\", \"init\"]:\n",
+    "                    counter += 1\n",
+    "                \n",
+    "                current_stage = \"down\"\n",
+    "            \n",
+    "            # Error detection\n",
+    "            # Knee angle\n",
+    "            analyze_knee_angle(mp_results=results, stage=current_stage, angle_thresholds=ANGLE_THRESHOLDS, draw_to_image=(image, video_dimensions))\n",
+    "\n",
+    "            # Knee over toe\n",
+    "            err_predicted_class = err_prediction_probabilities = err_prediction_probability = None\n",
+    "            if current_stage == \"down\":\n",
+    "                err_predicted_class = err_sklearn_model.predict(X)[0]\n",
+    "                err_prediction_probabilities = err_sklearn_model.predict_proba(X)[0]\n",
+    "                err_prediction_probability = round(err_prediction_probabilities[err_prediction_probabilities.argmax()], 2)\n",
+    "                \n",
+    "            \n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (800, 45), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display stage prediction\n",
+    "            cv2.putText(image, \"STAGE\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(stage_prediction_probability), (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (50, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display error prediction\n",
+    "            cv2.putText(image, \"K_O_T\", (200, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(err_prediction_probability), (195, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(err_predicted_class), (245, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display Counter\n",
+    "            cv2.putText(image, \"COUNTER\", (110, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(counter), (110, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "            traceback.print_exc()\n",
+    "            break\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Detection with deep learning model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/dp/err_lunge_dp.pkl\", \"rb\") as f:\n",
+    "    err_deep_learning_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n",
+      "No human found\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 15:17:20.031325: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH1)\n",
+    "current_stage = \"\"\n",
+    "counter = 0\n",
+    "\n",
+    "prediction_probability_threshold = 0.8\n",
+    "ANGLE_THRESHOLDS = [60, 135]\n",
+    "\n",
+    "knee_over_toe = False\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        video_dimensions = [image.shape[1], image.shape[0]]\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            stage_predicted_class = stage_sklearn_model.predict(X)[0]\n",
+    "            stage_prediction_probabilities = stage_sklearn_model.predict_proba(X)[0]\n",
+    "            stage_prediction_probability = round(stage_prediction_probabilities[stage_prediction_probabilities.argmax()], 2)\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if stage_predicted_class == \"I\" and stage_prediction_probability >= prediction_probability_threshold:\n",
+    "                current_stage = \"init\"\n",
+    "            elif stage_predicted_class == \"M\" and stage_prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"mid\"\n",
+    "            elif stage_predicted_class == \"D\" and stage_prediction_probability >= prediction_probability_threshold:\n",
+    "                if current_stage == \"mid\":\n",
+    "                    counter += 1\n",
+    "                \n",
+    "                current_stage = \"down\"\n",
+    "            \n",
+    "            # Error detection\n",
+    "            # Knee angle\n",
+    "            analyze_knee_angle(mp_results=results, stage=current_stage, angle_thresholds=ANGLE_THRESHOLDS, draw_to_image=(image, video_dimensions))\n",
+    "\n",
+    "            # Knee over toe\n",
+    "            err_predicted_class = err_prediction_probabilities = err_prediction_probability = None\n",
+    "            if current_stage == \"down\":\n",
+    "                err_prediction = err_deep_learning_model.predict(X, verbose=False)\n",
+    "                err_predicted_class = np.argmax(err_prediction, axis=1)[0]\n",
+    "                err_prediction_probability = round(max(err_prediction.tolist()[0]), 2)\n",
+    "\n",
+    "                err_predicted_class = \"C\" if err_predicted_class == 1 else \"L\"\n",
+    "                \n",
+    "            \n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (800, 45), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display stage prediction\n",
+    "            cv2.putText(image, \"STAGE\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(stage_prediction_probability), (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (50, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display error prediction\n",
+    "            cv2.putText(image, \"K_O_T\", (200, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(err_prediction_probability), (195, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(err_predicted_class), (245, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display Counter\n",
+    "            cv2.putText(image, \"COUNTER\", (110, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(counter), (110, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "            traceback.print_exc()\n",
+    "            break\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Conclusion\n",
+    "\n",
+    "- For stage detection:\n",
+    "    - Best Sklearn model: KNN\n",
+    "- For error detection:\n",
+    "    - Best Sklearn model: LR\n",
+    "    - Both models are correct most of the time"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/lunge_model/README.md b/core/lunge_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ce59167f714734ca682489ad60dce43436be9d74
--- /dev/null
+++ b/core/lunge_model/README.md
@@ -0,0 +1,43 @@
+<h2 align="center">BICEP CURL MODEL TRAINING PROCESS</h2>
+
+### 1. Folder structure
+
+```
+bicep_model
+│   1.stage.data.ipynb - process collected videos for lunge stage
+|   2.stage.sklearn.ipynb - train models using Sklearn ML algo for lunge stage
+│   3.stage.deep_leaning.ipynb - train models using Deep Learning for lunge stage
+│   4.stage.detection.ipynb - detection on test videos for lunge stage
+│   5.stage.data.ipynb - process collected videos for lunge stage
+|   6.err.sklearn.ipynb - train models using Sklearn ML algo for lunge error
+│   7.err.deep_leaning.ipynb - train models using Deep Learning for lunge error
+│   8.err.evaluation.ipynb - evaluate trained models for lunge error
+│   9.err.detection.ipynb - detection on test videos for lunge error
+|   stage.train.csv - train dataset for lunge stage after converted from videos
+|   stage.test.csv - test dataset for lunge stage after converted from videos
+|   err.train.csv - train dataset for lunge error after converted from videos
+|   err.test.csv - test dataset after for lunge error converted from videos
+|   evaluation.csv - models' evaluation results
+│
+└───model/ - folder contains best trained models and input scaler
+│   │
+```
+
+### 2. Important landmarks
+
+There are 3 popular errors of bicep curl that will be targeted in this thesis:
+
+-   Loose upper arm: when an arm moves upward during the exercise, the upper arm is moving instead of staying still.
+-   Weak peak contraction: when an arm moves upward, it does not go high enough therefore not put enough contraction to the bicep.
+-   Lean too far back: the performer’s torso leans back and fore during the exercise for momentum.
+
+In my research and exploration, **_the important MediaPipe Pose landmarks_** for this exercise are: nose, left shoulder, right shoulder, right elbow, left elbow, right wrist, left wrist, right hip and left hip.
+
+### 3. Error detection method
+
+1. **Knee angle**: Can be detected by calculating the angle of the left and right knee. To precisely choose the correct lower and upper thresholds for this error, videos of contributors perform correct form of the exercise are analyzed. In conclusion from the graph, the angle of left/right knee during the low position of a lunge should be in between 60 and 135 degrees.
+
+    - Analyzed result:
+      <p align="center"><img src="../../images/lunge_eval_4.png" alt="Logo" width="70%"></p>
+
+1. **Knee over toe**: Due to its complexity, machine learning will be used for this detection. See this [notebook](./8.err.evaluation.ipynb) for a evaluation process for this models.
diff --git a/core/lunge_model/err.evaluation.csv b/core/lunge_model/err.evaluation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1f53d2eaff2ac1ea9e29937ddad75cb0cc074070
--- /dev/null
+++ b/core/lunge_model/err.evaluation.csv
@@ -0,0 +1,23 @@
+Model,Precision Score,Accuracy Score,Recall Score,F1 Score,Confusion Matrix
+LR,0.9733139405601312,0.971996386630533,0.971996386630533,0.9719871073494147,"[[545   1]
+ [ 30 531]]"
+SGDC,0.9606281199356282,0.957542908762421,0.957542908762421,0.9574961020824345,"[[545   1]
+ [ 46 515]]"
+3_layers,0.9365072351551672,0.9277326106594399,0.9277326106594399,0.9274418797827825,"[[545   1]
+ [ 79 482]]"
+DTC,0.9192261367058114,0.916892502258356,0.916892502258356,0.9167297143761817,"[[479  67]
+ [ 25 536]]"
+7_layers_with_dropout,0.8920702972612289,0.8644986449864499,0.8644986449864499,0.8623537930008395,"[[544   2]
+ [148 413]]"
+RF,0.854529379025228,0.8419150858175248,0.8419150858175248,0.8407383790928666,"[[510  36]
+ [139 422]]"
+5_layers,0.8605088616622826,0.8310749774164409,0.8310749774164409,0.8279503932280722,"[[531  15]
+ [172 389]]"
+NB,0.7703551608827456,0.7687443541102078,0.7687443541102078,0.7682132184217674,"[[395 151]
+ [105 456]]"
+KNN,0.7684029707859914,0.7651309846431797,0.7651309846431797,0.7646522633908915,"[[445 101]
+ [159 402]]"
+7_layers,0.8379096785329353,0.7732610659439928,0.7732610659439928,0.7627451882949882,"[[541   5]
+ [246 315]]"
+SVC,0.751947122240204,0.7199638663053297,0.7199638663053297,0.7118153246676994,"[[488  58]
+ [252 309]]"
diff --git a/core/lunge_model/err.test.csv b/core/lunge_model/err.test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f7e42a65b38d4f8787a1ac54ffa1c3298c165bd6
--- /dev/null
+++ b/core/lunge_model/err.test.csv
@@ -0,0 +1,1108 @@
+label,nose_x,nose_y,nose_z,nose_v,left_shoulder_x,left_shoulder_y,left_shoulder_z,left_shoulder_v,right_shoulder_x,right_shoulder_y,right_shoulder_z,right_shoulder_v,left_hip_x,left_hip_y,left_hip_z,left_hip_v,right_hip_x,right_hip_y,right_hip_z,right_hip_v,left_knee_x,left_knee_y,left_knee_z,left_knee_v,right_knee_x,right_knee_y,right_knee_z,right_knee_v,left_ankle_x,left_ankle_y,left_ankle_z,left_ankle_v,right_ankle_x,right_ankle_y,right_ankle_z,right_ankle_v,left_heel_x,left_heel_y,left_heel_z,left_heel_v,right_heel_x,right_heel_y,right_heel_z,right_heel_v,left_foot_index_x,left_foot_index_y,left_foot_index_z,left_foot_index_v,right_foot_index_x,right_foot_index_y,right_foot_index_z,right_foot_index_v
+L,0.435578852891922,0.4953083395957947,-0.03606807813048363,0.9999067187309265,0.49325063824653625,0.4626350998878479,-0.19183605909347534,0.9999914169311523,0.4865393042564392,0.49438005685806274,0.15582984685897827,0.9998165369033813,0.5916197299957275,0.6545447111129761,-0.10790077596902847,0.9999886155128479,0.5669006109237671,0.6538147926330566,0.10778363049030304,0.9999709129333496,0.6284436583518982,0.8254538774490356,-0.18316006660461426,0.9997404217720032,0.47001373767852783,0.647714376449585,0.2098924070596695,0.8702393174171448,0.7389580607414246,0.78090500831604,-0.17115704715251923,0.9992231130599976,0.5159229636192322,0.8001373410224915,0.22154809534549713,0.9417077302932739,0.7568104267120361,0.7572186589241028,-0.1697283387184143,0.9917322397232056,0.5361555218696594,0.8268635272979736,0.22000856697559357,0.8978049159049988,0.7573235034942627,0.8527395725250244,-0.2163763791322708,0.9981622695922852,0.4768531620502472,0.8444119691848755,0.21555565297603607,0.9356915950775146
+L,0.43077924847602844,0.4967392683029175,-0.041445616632699966,0.9999434947967529,0.484919011592865,0.4723358750343323,-0.19546054303646088,0.9999930262565613,0.48311054706573486,0.49646303057670593,0.1527196168899536,0.9998487830162048,0.5908106565475464,0.6573281288146973,-0.10851886868476868,0.999994695186615,0.5639892220497131,0.6541271209716797,0.10837594419717789,0.9999760389328003,0.62810879945755,0.8290455341339111,-0.18967290222644806,0.999839723110199,0.4688416123390198,0.6506074666976929,0.2169961929321289,0.865271270275116,0.7393418550491333,0.7812426686286926,-0.18365883827209473,0.9994269013404846,0.5162205696105957,0.8084933161735535,0.2330382615327835,0.9358725547790527,0.7552783489227295,0.7548032402992249,-0.18253004550933838,0.9942334890365601,0.5355764031410217,0.835247814655304,0.23163339495658875,0.8892433643341064,0.7623982429504395,0.8548069000244141,-0.2196855992078781,0.9986553192138672,0.4767022728919983,0.8490899205207825,0.23273222148418427,0.9381647109985352
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diff --git a/core/lunge_model/knee_angle_2.csv b/core/lunge_model/knee_angle_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f739788b05075d8b3990746d0b511eeb39b199cc
--- /dev/null
+++ b/core/lunge_model/knee_angle_2.csv
@@ -0,0 +1,9907 @@
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diff --git a/core/lunge_model/stage.train.csv b/core/lunge_model/stage.train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c763257d8f97ae7b457544713af6a1259447e6ea
--- /dev/null
+++ b/core/lunge_model/stage.train.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe9c1a432b8ca18f116d4d76785c95976fe783ec164c67c7d7fb14477a0c3752
+size 24142502
diff --git a/core/plank_model/1.data.ipynb b/core/plank_model/1.data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ca209a1a6fea03c2fb2c25bb2ce4057c4dae871a
--- /dev/null
+++ b/core/plank_model/1.data.ipynb
@@ -0,0 +1,706 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[67861]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x104544860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e480). One of the two will be used. Which one is undefined.\n",
+      "objc[67861]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103324a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[67861]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103324a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[67861]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x103324ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Make some pose detections (Test if Everything works correctly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(\"../data/plank/20221011_202153.mp4\")\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Build dataset from collected videos and picture from Kaggle to .csv file for dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1 Determine important keypoints and set up important functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 3 stages that I try to classify for Plank Exercise Correction:\n",
+    "\n",
+    "- Correct: \"C\"\n",
+    "- Back is too low: \"L\"\n",
+    "- Back is too high: \"H\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Set up important functions*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "    \n",
+    "\n",
+    "def init_csv(dataset_path: str):\n",
+    "    '''\n",
+    "    Create a blank csv file with just columns\n",
+    "    '''\n",
+    "\n",
+    "    # Ignore if file is already exist\n",
+    "    if os.path.exists(dataset_path):\n",
+    "        return\n",
+    "\n",
+    "    # Write all the columns to a empaty file\n",
+    "    with open(dataset_path, mode=\"w\", newline=\"\") as f:\n",
+    "        csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "        csv_writer.writerow(HEADERS)\n",
+    "\n",
+    "\n",
+    "def export_landmark_to_csv(dataset_path: str, results, action: str) -> None:\n",
+    "    '''\n",
+    "    Export Labeled Data from detected landmark to csv\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "    keypoints = []\n",
+    "\n",
+    "    try:\n",
+    "        # Extract coordinate of important landmarks\n",
+    "        for lm in IMPORTANT_LMS:\n",
+    "            keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "            keypoints.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "        \n",
+    "        keypoints = list(np.array(keypoints).flatten())\n",
+    "\n",
+    "        # Insert action as the label (first column)\n",
+    "        keypoints.insert(0, action)\n",
+    "\n",
+    "        # Append new row to .csv file\n",
+    "        with open(dataset_path, mode=\"a\", newline=\"\") as f:\n",
+    "            csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "            csv_writer.writerow(keypoints)\n",
+    "        \n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_train.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "    \n",
+    "\n",
+    "def concat_csv_files_with_same_headers(file_paths: list, saved_path: str):\n",
+    "    '''\n",
+    "    Concat different csv files\n",
+    "    '''\n",
+    "    all_df = []\n",
+    "    for path in file_paths:\n",
+    "        df = pd.read_csv(path, index_col=None, header=0)\n",
+    "        all_df.append(df)\n",
+    "    \n",
+    "    results = pd.concat(all_df, axis=0, ignore_index=True)\n",
+    "    results.to_csv(saved_path, sep=',', encoding='utf-8', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2 Extract from video\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATASET_PATH = \"train.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/plank/bad/plank_bad_high_4.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "# init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # Press C to save as correct form\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as low back\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as high back\n",
+    "        elif k == ord(\"h\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"H\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 28520 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "C    9904\n",
+      "L    9546\n",
+      "H    9070\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# csv_files = [os.path.join(\"./\", f) for f in os.listdir(\"./\") if \"csv\" in f]\n",
+    "\n",
+    "# concat_csv_files_with_same_headers(csv_files, \"train.csv\")\n",
+    "\n",
+    "# remove_duplicate_rows(DATASET_PATH)\n",
+    "\n",
+    "df = describe_dataset(DATASET_PATH)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3. Extract from Kaggle dataset ([download here](https://www.kaggle.com/datasets/niharika41298/yoga-poses-dataset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FOLDER_PATH = \"../data/kaggle/TRAIN/plank\"\n",
+    "picture_files = [os.path.join(FOLDER_PATH, f) for f in os.listdir(FOLDER_PATH) if os.path.isfile(os.path.join(FOLDER_PATH, f))]\n",
+    "print(f\"Total pictures: {len(picture_files)}\")\n",
+    "\n",
+    "DATASET_PATH = \"./kaggle.csv\"\n",
+    "saved_counts = 0\n",
+    "\n",
+    "init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.7, min_tracking_confidence=0.5) as pose:\n",
+    "    index = 0\n",
+    "    \n",
+    "    while True:\n",
+    "        if index == len(picture_files):\n",
+    "            break\n",
+    "        \n",
+    "        file_path = picture_files[index]\n",
+    "\n",
+    "        image = cv2.imread(file_path)\n",
+    "\n",
+    "        # Flip image horizontally for more data\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # get dimensions of image\n",
+    "        dimensions = image.shape\n",
+    "        \n",
+    "        # height, width, number of channels in image\n",
+    "        height = image.shape[0]\n",
+    "        width = image.shape[1]\n",
+    "        channels = image.shape[2]\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        if width > 1000:\n",
+    "            image = rescale_frame(image, 60)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {saved_counts}\", (20, 20), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        if k == ord('d'): \n",
+    "            index += 1\n",
+    "\n",
+    "        elif k == ord(\"s\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"C\")\n",
+    "            saved_counts += 1\n",
+    "\n",
+    "        elif k == ord(\"f\"):\n",
+    "            index += 1\n",
+    "            os.remove(file_path)\n",
+    "\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "\n",
+    "        else:\n",
+    "            continue\n",
+    "\n",
+    "        # # Press Q to close cv2 window\n",
+    "        # if cv2.waitKey(1) & 0xFF == ord('d'):\n",
+    "        #     index += 1\n",
+    "\n",
+    "        # # Press Q to close cv2 window\n",
+    "        # if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "        #     break\n",
+    "\n",
+    "    # Close cv2 window\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Refine Data & Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='label', ylabel='count'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='label', data=df, palette=\"Set1\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Extract data for test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
+     ]
+    }
+   ],
+   "source": [
+    "TEST_DATASET_PATH = \"test.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/plank/plank_test_4.mp4\")\n",
+    "save_counts = 0\n",
+    "\n",
+    "# init_csv(TEST_DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks: continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"Saved: {save_counts}\", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # Press C to save as correct form\n",
+    "        if k == ord('c'): \n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"C\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as low back\n",
+    "        elif k == ord(\"l\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"L\")\n",
+    "            save_counts += 1\n",
+    "        # Press L to save as high back\n",
+    "        elif k == ord(\"h\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"H\")\n",
+    "            save_counts += 1\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 710 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "H    241\n",
+      "L    235\n",
+      "C    234\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(TEST_DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='count', ylabel='label'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(y='label', data=test_df, palette=\"Set1\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/plank_model/2.sklearn.ipynb b/core/plank_model/2.sklearn.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9acb1bb26b546a52009252175501d1b3d54e726f
--- /dev/null
+++ b/core/plank_model/2.sklearn.ipynb
@@ -0,0 +1,762 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Train Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.1. Describe data and split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 28520 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "C    9904\n",
+      "L    9546\n",
+      "H    9070\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>nose_x</th>\n",
+       "      <th>nose_y</th>\n",
+       "      <th>nose_z</th>\n",
+       "      <th>nose_v</th>\n",
+       "      <th>left_shoulder_x</th>\n",
+       "      <th>left_shoulder_y</th>\n",
+       "      <th>left_shoulder_z</th>\n",
+       "      <th>left_shoulder_v</th>\n",
+       "      <th>right_shoulder_x</th>\n",
+       "      <th>...</th>\n",
+       "      <th>right_heel_z</th>\n",
+       "      <th>right_heel_v</th>\n",
+       "      <th>left_foot_index_x</th>\n",
+       "      <th>left_foot_index_y</th>\n",
+       "      <th>left_foot_index_z</th>\n",
+       "      <th>left_foot_index_v</th>\n",
+       "      <th>right_foot_index_x</th>\n",
+       "      <th>right_foot_index_y</th>\n",
+       "      <th>right_foot_index_z</th>\n",
+       "      <th>right_foot_index_v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28517</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.735630</td>\n",
+       "      <td>0.543294</td>\n",
+       "      <td>0.007467</td>\n",
+       "      <td>0.999246</td>\n",
+       "      <td>0.695831</td>\n",
+       "      <td>0.417349</td>\n",
+       "      <td>0.155194</td>\n",
+       "      <td>0.995723</td>\n",
+       "      <td>0.720067</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.086010</td>\n",
+       "      <td>0.966131</td>\n",
+       "      <td>0.226601</td>\n",
+       "      <td>0.598075</td>\n",
+       "      <td>0.219305</td>\n",
+       "      <td>0.470830</td>\n",
+       "      <td>0.220079</td>\n",
+       "      <td>0.614120</td>\n",
+       "      <td>0.026265</td>\n",
+       "      <td>0.934942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28518</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.775572</td>\n",
+       "      <td>0.517579</td>\n",
+       "      <td>0.012821</td>\n",
+       "      <td>0.999378</td>\n",
+       "      <td>0.704168</td>\n",
+       "      <td>0.404210</td>\n",
+       "      <td>0.162908</td>\n",
+       "      <td>0.995909</td>\n",
+       "      <td>0.730823</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.070911</td>\n",
+       "      <td>0.967070</td>\n",
+       "      <td>0.238810</td>\n",
+       "      <td>0.610591</td>\n",
+       "      <td>0.198591</td>\n",
+       "      <td>0.496140</td>\n",
+       "      <td>0.228907</td>\n",
+       "      <td>0.625559</td>\n",
+       "      <td>0.018591</td>\n",
+       "      <td>0.938905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28519</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.790600</td>\n",
+       "      <td>0.498958</td>\n",
+       "      <td>0.007789</td>\n",
+       "      <td>0.999467</td>\n",
+       "      <td>0.710651</td>\n",
+       "      <td>0.394019</td>\n",
+       "      <td>0.164441</td>\n",
+       "      <td>0.996123</td>\n",
+       "      <td>0.736771</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.085872</td>\n",
+       "      <td>0.967943</td>\n",
+       "      <td>0.238197</td>\n",
+       "      <td>0.609329</td>\n",
+       "      <td>0.233198</td>\n",
+       "      <td>0.510583</td>\n",
+       "      <td>0.227823</td>\n",
+       "      <td>0.626068</td>\n",
+       "      <td>0.036127</td>\n",
+       "      <td>0.940917</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 69 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      label    nose_x    nose_y    nose_z    nose_v  left_shoulder_x  \\\n",
+       "28517     1  0.735630  0.543294  0.007467  0.999246         0.695831   \n",
+       "28518     1  0.775572  0.517579  0.012821  0.999378         0.704168   \n",
+       "28519     1  0.790600  0.498958  0.007789  0.999467         0.710651   \n",
+       "\n",
+       "       left_shoulder_y  left_shoulder_z  left_shoulder_v  right_shoulder_x  \\\n",
+       "28517         0.417349         0.155194         0.995723          0.720067   \n",
+       "28518         0.404210         0.162908         0.995909          0.730823   \n",
+       "28519         0.394019         0.164441         0.996123          0.736771   \n",
+       "\n",
+       "       ...  right_heel_z  right_heel_v  left_foot_index_x  left_foot_index_y  \\\n",
+       "28517  ...      0.086010      0.966131           0.226601           0.598075   \n",
+       "28518  ...      0.070911      0.967070           0.238810           0.610591   \n",
+       "28519  ...      0.085872      0.967943           0.238197           0.609329   \n",
+       "\n",
+       "       left_foot_index_z  left_foot_index_v  right_foot_index_x  \\\n",
+       "28517           0.219305           0.470830            0.220079   \n",
+       "28518           0.198591           0.496140            0.228907   \n",
+       "28519           0.233198           0.510583            0.227823   \n",
+       "\n",
+       "       right_foot_index_y  right_foot_index_z  right_foot_index_v  \n",
+       "28517            0.614120            0.026265            0.934942  \n",
+       "28518            0.625559            0.018591            0.938905  \n",
+       "28519            0.626068            0.036127            0.940917  \n",
+       "\n",
+       "[3 rows x 69 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = describe_dataset(\"./train.csv\")\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"H\", \"label\"] = 1\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 2\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract features and class\n",
+    "X = df.drop(\"label\", axis=1)\n",
+    "y = df[\"label\"].astype(\"int\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler()\n",
+    "X = pd.DataFrame(sc.fit_transform(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1469    0\n",
+       "292     0\n",
+       "1568    0\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
+    "y_test.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2. Train model using Scikit-Learn and train set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "algorithms =[(\"LR\", LogisticRegression()),\n",
+    "         (\"SVC\", SVC(probability=True)),\n",
+    "         ('KNN',KNeighborsClassifier()),\n",
+    "         (\"DTC\", DecisionTreeClassifier()),\n",
+    "         (\"SGDC\", CalibratedClassifierCV(SGDClassifier())),\n",
+    "         (\"NB\", GaussianNB()),\n",
+    "         ('RF', RandomForestClassifier()),]\n",
+    "\n",
+    "models = {}\n",
+    "final_results = []\n",
+    "\n",
+    "for name, model in algorithms:\n",
+    "    trained_model = model.fit(X_train, y_train)\n",
+    "    models[name] = trained_model\n",
+    "\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(X_test)\n",
+    "\n",
+    "    p_score = precision_score(y_test, model_results, average=None, labels=[0, 1, 2])\n",
+    "    a_score = accuracy_score(y_test, model_results)\n",
+    "    r_score = recall_score(y_test, model_results, average=None, labels=[0, 1, 2])\n",
+    "    f1_score_result = f1_score(y_test, model_results, average=None, labels=[0, 1, 2])\n",
+    "    cm = confusion_matrix(y_test, model_results, labels=[0, 1, 2])\n",
+    "    final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[0.999, 1.0, 1.0]</td>\n",
+       "      <td>0.999825</td>\n",
+       "      <td>[1.0, 1.0, 0.999]</td>\n",
+       "      <td>[1.0, 1.0, 1.0]</td>\n",
+       "      <td>[[1915, 0, 0], [0, 1844, 0], [1, 0, 1944]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>0.999649</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[0.998, 1.0, 0.999]</td>\n",
+       "      <td>0.999299</td>\n",
+       "      <td>[0.999, 1.0, 0.998]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1914, 0, 1], [0, 1844, 0], [3, 0, 1942]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.998, 1.0, 1.0]</td>\n",
+       "      <td>0.999474</td>\n",
+       "      <td>[1.0, 0.999, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1915, 0, 0], [1, 1843, 0], [2, 0, 1943]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.999, 0.998, 0.999]</td>\n",
+       "      <td>0.998597</td>\n",
+       "      <td>[0.997, 1.0, 0.999]</td>\n",
+       "      <td>[0.998, 0.999, 0.999]</td>\n",
+       "      <td>[[1909, 4, 2], [0, 1844, 0], [2, 0, 1943]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.994, 1.0, 0.999]</td>\n",
+       "      <td>0.997721</td>\n",
+       "      <td>[0.999, 0.998, 0.995]</td>\n",
+       "      <td>[0.997, 0.999, 0.997]</td>\n",
+       "      <td>[[1914, 0, 1], [3, 1841, 0], [9, 0, 1936]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.816, 0.931, 0.941]</td>\n",
+       "      <td>0.892532</td>\n",
+       "      <td>[0.883, 0.951, 0.847]</td>\n",
+       "      <td>[0.848, 0.941, 0.892]</td>\n",
+       "      <td>[[1690, 122, 103], [91, 1753, 0], [290, 7, 1648]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model        Precision Score  Accuracy score           Recall Score  \\\n",
+       "0   KNN      [0.999, 1.0, 1.0]        0.999825      [1.0, 1.0, 0.999]   \n",
+       "1    LR    [0.999, 1.0, 0.999]        0.999649    [0.999, 1.0, 0.999]   \n",
+       "2   SVC    [0.998, 1.0, 0.999]        0.999299    [0.999, 1.0, 0.998]   \n",
+       "3    RF      [0.998, 1.0, 1.0]        0.999474    [1.0, 0.999, 0.999]   \n",
+       "4  SGDC  [0.999, 0.998, 0.999]        0.998597    [0.997, 1.0, 0.999]   \n",
+       "5   DTC    [0.994, 1.0, 0.999]        0.997721  [0.999, 0.998, 0.995]   \n",
+       "6    NB  [0.816, 0.931, 0.941]        0.892532  [0.883, 0.951, 0.847]   \n",
+       "\n",
+       "                F1 score                                   Confusion Matrix  \n",
+       "0        [1.0, 1.0, 1.0]         [[1915, 0, 0], [0, 1844, 0], [1, 0, 1944]]  \n",
+       "1    [0.999, 1.0, 0.999]         [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]  \n",
+       "2    [0.999, 1.0, 0.999]         [[1914, 0, 1], [0, 1844, 0], [3, 0, 1942]]  \n",
+       "3    [0.999, 1.0, 0.999]         [[1915, 0, 0], [1, 1843, 0], [2, 0, 1943]]  \n",
+       "4  [0.998, 0.999, 0.999]         [[1909, 4, 2], [0, 1844, 0], [2, 0, 1943]]  \n",
+       "5  [0.997, 0.999, 0.997]         [[1914, 0, 1], [3, 1841, 0], [9, 0, 1936]]  \n",
+       "6  [0.848, 0.941, 0.892]  [[1690, 122, 103], [91, 1753, 0], [290, 7, 1648]]  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort results by F1 score\n",
+    "final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "\n",
+    "pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3. Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 710 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "H    241\n",
+      "L    235\n",
+      "C    234\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(\"./test.csv\")\n",
+    "test_df = test_df.sample(frac=1).reset_index(drop=True)\n",
+    "\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"H\", \"label\"] = 1\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 2\n",
+    "\n",
+    "test_x = test_df.drop(\"label\", axis=1)\n",
+    "test_y = test_df[\"label\"].astype(\"int\")\n",
+    "\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.987, 1.0, 1.0]</td>\n",
+       "      <td>0.995775</td>\n",
+       "      <td>[1.0, 0.996, 0.991]</td>\n",
+       "      <td>[0.994, 0.998, 0.996]</td>\n",
+       "      <td>[[234, 0, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[0.963, 1.0, 1.0]</td>\n",
+       "      <td>0.987324</td>\n",
+       "      <td>[1.0, 0.992, 0.97]</td>\n",
+       "      <td>[0.981, 0.996, 0.985]</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [7, 0, 228]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.974, 0.975, 0.996]</td>\n",
+       "      <td>0.981690</td>\n",
+       "      <td>[0.974, 0.983, 0.987]</td>\n",
+       "      <td>[0.974, 0.979, 0.991]</td>\n",
+       "      <td>[[228, 6, 0], [3, 237, 1], [3, 0, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[0.869, 0.996, 1.0]</td>\n",
+       "      <td>0.949296</td>\n",
+       "      <td>[0.996, 0.992, 0.86]</td>\n",
+       "      <td>[0.928, 0.994, 0.924]</td>\n",
+       "      <td>[[233, 1, 0], [2, 239, 0], [33, 0, 202]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.765, 1.0, 1.0]</td>\n",
+       "      <td>0.898592</td>\n",
+       "      <td>[1.0, 1.0, 0.694]</td>\n",
+       "      <td>[0.867, 1.0, 0.819]</td>\n",
+       "      <td>[[234, 0, 0], [0, 241, 0], [72, 0, 163]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.892, 0.737, 0.945]</td>\n",
+       "      <td>0.842254</td>\n",
+       "      <td>[0.632, 0.942, 0.949]</td>\n",
+       "      <td>[0.74, 0.827, 0.947]</td>\n",
+       "      <td>[[148, 73, 13], [14, 227, 0], [4, 8, 223]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.69, 1.0, 0.625]</td>\n",
+       "      <td>0.767606</td>\n",
+       "      <td>[0.543, 0.988, 0.766]</td>\n",
+       "      <td>[0.608, 0.994, 0.688]</td>\n",
+       "      <td>[[127, 0, 107], [2, 238, 1], [55, 0, 180]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model        Precision Score  Accuracy score           Recall Score  \\\n",
+       "0    LR      [0.987, 1.0, 1.0]        0.995775    [1.0, 0.996, 0.991]   \n",
+       "1   SVC      [0.963, 1.0, 1.0]        0.987324     [1.0, 0.992, 0.97]   \n",
+       "2  SGDC  [0.974, 0.975, 0.996]        0.981690  [0.974, 0.983, 0.987]   \n",
+       "3   KNN    [0.869, 0.996, 1.0]        0.949296   [0.996, 0.992, 0.86]   \n",
+       "4    RF      [0.765, 1.0, 1.0]        0.898592      [1.0, 1.0, 0.694]   \n",
+       "5    NB  [0.892, 0.737, 0.945]        0.842254  [0.632, 0.942, 0.949]   \n",
+       "6   DTC     [0.69, 1.0, 0.625]        0.767606  [0.543, 0.988, 0.766]   \n",
+       "\n",
+       "                F1 score                            Confusion Matrix  \n",
+       "0  [0.994, 0.998, 0.996]     [[234, 0, 0], [1, 240, 0], [2, 0, 233]]  \n",
+       "1  [0.981, 0.996, 0.985]     [[234, 0, 0], [2, 239, 0], [7, 0, 228]]  \n",
+       "2  [0.974, 0.979, 0.991]     [[228, 6, 0], [3, 237, 1], [3, 0, 232]]  \n",
+       "3  [0.928, 0.994, 0.924]    [[233, 1, 0], [2, 239, 0], [33, 0, 202]]  \n",
+       "4    [0.867, 1.0, 0.819]    [[234, 0, 0], [0, 241, 0], [72, 0, 163]]  \n",
+       "5   [0.74, 0.827, 0.947]  [[148, 73, 13], [14, 227, 0], [4, 8, 223]]  \n",
+       "6  [0.608, 0.994, 0.688]  [[127, 0, 107], [2, 238, 1], [55, 0, 180]]  "
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=None, labels=[0, 1, 2])\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=None, labels=[0, 1, 2])\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=None, labels=[0, 1, 2])\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[0, 1, 2])\n",
+    "    testset_final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm ))\n",
+    "\n",
+    "\n",
+    "testset_final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.4. Dumped model and input scaler using pickle\n",
+    "\n",
+    "According to the evaluations, there are multiple good models at the moment, therefore, the best models are LR and Ridge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/all_sklearn.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/LR_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"LR\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/SVC_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"SVC\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dump input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(sc, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/plank_model/3.deep_learning.ipynb b/core/plank_model/3.deep_learning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0c350550b4cc41ef5e0907c4aef426601e87ba32
--- /dev/null
+++ b/core/plank_model/3.deep_learning.ipynb
@@ -0,0 +1,1394 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data visualization\n",
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "\n",
+    "# Keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "from keras.callbacks import EarlyStopping\n",
+    "import keras_tuner as kt\n",
+    "\n",
+    "# Train-Test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# Classification Report\n",
+    "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Describe Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 28520 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "C    9904\n",
+      "L    9546\n",
+      "H    9070\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "# Remove duplicate rows (optional)\n",
+    "def remove_duplicate_rows(dataset_path: str):\n",
+    "    '''\n",
+    "    Remove duplicated data from the dataset then save it to another files\n",
+    "    '''\n",
+    "    \n",
+    "    df = pd.read_csv(dataset_path)\n",
+    "    df.drop_duplicates(keep=\"first\", inplace=True)\n",
+    "    df.to_csv(f\"cleaned_train.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))\n",
+    "\n",
+    "\n",
+    "df = describe_dataset(\"./train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Preprocess Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows: 28520 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "0    9904\n",
+      "2    9546\n",
+      "1    9070\n",
+      "Name: label, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "df = pd.read_csv(\"./train.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"H\", \"label\"] = 1\n",
+    "df.loc[df[\"label\"] == \"L\", \"label\"] = 2\n",
+    "\n",
+    "print(f'Number of rows: {df.shape[0]} \\nNumber of columns: {df.shape[1]}\\n')\n",
+    "print(f\"Labels: \\n{df['label'].value_counts()}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "# Dump input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f2:\n",
+    "    sc = pickle.load(f2)\n",
+    "\n",
+    "x = df.drop(\"label\", axis = 1)\n",
+    "x = pd.DataFrame(sc.transform(x))\n",
+    "\n",
+    "y = df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2, random_state=1234)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Build Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Set up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop_early = EarlyStopping(monitor='loss', patience=3)\n",
+    "\n",
+    "# Final Results\n",
+    "final_models = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_model(model):\n",
+    "    '''\n",
+    "    Describe Model architecture\n",
+    "    '''\n",
+    "    print(f\"Describe models architecture\")\n",
+    "    for i, layer in enumerate(model.layers):\n",
+    "        number_of_units = layer.units if hasattr(layer, 'units') else 0\n",
+    "\n",
+    "        if hasattr(layer, \"activation\"):\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: \", layer.activation)\n",
+    "        else:\n",
+    "            print(f\"Layer-{i + 1}: {number_of_units} units, func: None\")\n",
+    "            \n",
+    "\n",
+    "def get_best_model(tuner):\n",
+    "    '''\n",
+    "    Describe and return the best model found from keras tuner\n",
+    "    '''\n",
+    "    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+    "    best_model = tuner.hypermodel.build(best_hps)\n",
+    "\n",
+    "    describe_model(best_model)\n",
+    "\n",
+    "    print(\"\\nOther params:\")\n",
+    "    ignore_params = [\"tuner\", \"activation\", \"layer\"]\n",
+    "    for param, value in best_hps.values.items():\n",
+    "        if not any(word in param for word in ignore_params):\n",
+    "            print(f\"{param}: {value}\")\n",
+    "\n",
+    "    return best_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Model with 3 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_3l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(3, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 00m 55s]\n",
+      "accuracy: 0.9989480972290039\n",
+      "\n",
+      "Best accuracy So Far: 0.9990357756614685\n",
+      "Total elapsed time: 00h 11m 35s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_3l = kt.Hyperband(\n",
+    "    model_3l_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo'\n",
+    ")\n",
+    "tuner_3l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-2: 32 units, func:  <function tanh at 0x17f4a2670>\n",
+      "Layer-3: 3 units, func:  <function softmax at 0x17f49d8b0>\n",
+      "learning_rate: 0.001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 22:34:40.154500: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - ETA: 0s - loss: 0.0489 - accuracy: 0.9877"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 22:35:00.478855: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - 24s 10ms/step - loss: 0.0489 - accuracy: 0.9877 - val_loss: 0.0067 - val_accuracy: 0.9986\n",
+      "Epoch 2/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.0045 - val_accuracy: 0.9989\n",
+      "Epoch 3/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0100 - accuracy: 0.9976 - val_loss: 0.0076 - val_accuracy: 0.9979\n",
+      "Epoch 4/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0063 - accuracy: 0.9982 - val_loss: 0.0038 - val_accuracy: 0.9986\n",
+      "Epoch 5/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.0012 - val_accuracy: 0.9996\n",
+      "Epoch 6/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0058 - accuracy: 0.9985 - val_loss: 0.0014 - val_accuracy: 0.9993\n",
+      "Epoch 7/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 0.0034 - val_accuracy: 0.9989\n",
+      "Epoch 8/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 9.3191e-04 - val_accuracy: 0.9996\n",
+      "Epoch 9/100\n",
+      "2282/2282 [==============================] - 22s 10ms/step - loss: 0.0052 - accuracy: 0.9989 - val_loss: 0.0023 - val_accuracy: 0.9993\n",
+      "Epoch 10/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.0023 - val_accuracy: 0.9995\n",
+      "Epoch 11/100\n",
+      "2282/2282 [==============================] - 24s 10ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.0012 - val_accuracy: 0.9995\n",
+      "Epoch 12/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.0034 - val_accuracy: 0.9988\n",
+      "Epoch 13/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0021 - val_accuracy: 0.9991\n",
+      "Epoch 14/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
+      "Epoch 15/100\n",
+      "2282/2282 [==============================] - 24s 11ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0046 - val_accuracy: 0.9982\n",
+      "Epoch 16/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0049 - accuracy: 0.9989 - val_loss: 9.1994e-04 - val_accuracy: 0.9998\n",
+      "Epoch 17/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0037 - accuracy: 0.9992 - val_loss: 0.0042 - val_accuracy: 0.9984\n",
+      "Epoch 18/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 9.2398e-04 - val_accuracy: 0.9996\n",
+      "Epoch 19/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0063 - val_accuracy: 0.9982\n",
+      "Epoch 20/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0015 - val_accuracy: 0.9995\n",
+      "Epoch 21/100\n",
+      "2282/2282 [==============================] - 24s 10ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.0011 - val_accuracy: 0.9996\n",
+      "Epoch 22/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.0040 - val_accuracy: 0.9991\n",
+      "Epoch 23/100\n",
+      "2282/2282 [==============================] - 23s 10ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0019 - val_accuracy: 0.9996\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x28cbaa700>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_3l = get_best_model(tuner_3l)\n",
+    "model_3l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"3_layers\"] = model_3l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Model with 5 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_5l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(3, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 01m 07s]\n",
+      "accuracy: 0.9977647066116333\n",
+      "\n",
+      "Best accuracy So Far: 0.9995178580284119\n",
+      "Total elapsed time: 00h 14m 00s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_5l = kt.Hyperband(\n",
+    "    model_5l_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_1'\n",
+    ")\n",
+    "tuner_5l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-2: 96 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-4: 320 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-5: 3 units, func:  <function softmax at 0x17f49d8b0>\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n",
+      "   1/2282 [..............................] - ETA: 12:30 - loss: 1.1718 - accuracy: 0.3000"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 23:03:14.751713: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9754"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 23:03:38.016530: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - 28s 12ms/step - loss: 0.0861 - accuracy: 0.9754 - val_loss: 0.0077 - val_accuracy: 0.9988\n",
+      "Epoch 2/100\n",
+      "2282/2282 [==============================] - 28s 12ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.0114 - val_accuracy: 0.9977\n",
+      "Epoch 3/100\n",
+      "2282/2282 [==============================] - 26s 12ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.0027 - val_accuracy: 0.9993\n",
+      "Epoch 4/100\n",
+      "2282/2282 [==============================] - 26s 11ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.0021 - val_accuracy: 0.9993\n",
+      "Epoch 5/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 9.3464e-04 - val_accuracy: 0.9996\n",
+      "Epoch 6/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
+      "Epoch 7/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0045 - accuracy: 0.9989 - val_loss: 0.0021 - val_accuracy: 0.9991\n",
+      "Epoch 8/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0031 - val_accuracy: 0.9986\n",
+      "Epoch 9/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 9.4086e-04 - val_accuracy: 0.9996\n",
+      "Epoch 10/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 7.7891e-04 - val_accuracy: 0.9996\n",
+      "Epoch 11/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
+      "Epoch 12/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0017 - val_accuracy: 0.9993\n",
+      "Epoch 13/100\n",
+      "2282/2282 [==============================] - 28s 12ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9995\n",
+      "Epoch 14/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9995\n",
+      "Epoch 15/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 8.1616e-04 - val_accuracy: 0.9996\n",
+      "Epoch 16/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 6.3959e-04 - val_accuracy: 0.9998\n",
+      "Epoch 17/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
+      "Epoch 18/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9993\n",
+      "Epoch 19/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0013 - val_accuracy: 0.9996\n",
+      "Epoch 20/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0025 - val_accuracy: 0.9988\n",
+      "Epoch 21/100\n",
+      "2282/2282 [==============================] - 27s 12ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x307417f70>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_5l = get_best_model(tuner_5l)\n",
+    "model_5l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"5_layers\"] = model_5l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4. Model with 7 layers (along with Dropout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_7lD_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_dropout_1 = hp.Float('dropout_1', min_value=0.1, max_value=0.5, step=0.1)\n",
+    "    hp_dropout_2 = hp.Float('dropout_2', min_value=0.1, max_value=0.5, step=0.1)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dropout(rate=hp_dropout_1))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dropout(rate=hp_dropout_2))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(3, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Reloading Oracle from existing project keras_tuner_dir/keras_tuner_demo_4/oracle.json\n",
+      "INFO:tensorflow:Reloading Tuner from keras_tuner_dir/keras_tuner_demo_4/tuner0.json\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_7lD = kt.Hyperband(\n",
+    "    model_7lD_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_4'\n",
+    ")\n",
+    "tuner_7lD.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-2: 480 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 256 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 480 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-7: 3 units, func:  <function softmax at 0x17f49d8b0>\n",
+      "\n",
+      "Other params:\n",
+      "dropout_1: 0.30000000000000004\n",
+      "dropout_2: 0.4\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 23:59:17.522675: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - ETA: 0s - loss: 0.0917 - accuracy: 0.9722"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-22 23:59:44.220298: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - 31s 13ms/step - loss: 0.0917 - accuracy: 0.9722 - val_loss: 0.0082 - val_accuracy: 0.9984\n",
+      "Epoch 2/100\n",
+      "2282/2282 [==============================] - 31s 14ms/step - loss: 0.0121 - accuracy: 0.9968 - val_loss: 0.0067 - val_accuracy: 0.9988\n",
+      "Epoch 3/100\n",
+      "2282/2282 [==============================] - 29s 13ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.0134 - val_accuracy: 0.9965\n",
+      "Epoch 4/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0083 - accuracy: 0.9981 - val_loss: 0.0051 - val_accuracy: 0.9989\n",
+      "Epoch 5/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0060 - accuracy: 0.9986 - val_loss: 0.0025 - val_accuracy: 0.9996\n",
+      "Epoch 6/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0072 - accuracy: 0.9983 - val_loss: 0.0046 - val_accuracy: 0.9995\n",
+      "Epoch 7/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.0024 - val_accuracy: 0.9993\n",
+      "Epoch 8/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.0041 - val_accuracy: 0.9995\n",
+      "Epoch 9/100\n",
+      "2282/2282 [==============================] - 31s 13ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
+      "Epoch 10/100\n",
+      "2282/2282 [==============================] - 31s 14ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
+      "Epoch 11/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.0021 - val_accuracy: 0.9993\n",
+      "Epoch 12/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
+      "Epoch 13/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0012 - val_accuracy: 0.9995\n",
+      "Epoch 14/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9977\n",
+      "Epoch 15/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
+      "Epoch 16/100\n",
+      "2282/2282 [==============================] - 31s 13ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0014 - val_accuracy: 0.9995\n",
+      "Epoch 17/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 6.3795e-04 - val_accuracy: 0.9996\n",
+      "Epoch 18/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
+      "Epoch 19/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0028 - val_accuracy: 0.9993\n",
+      "Epoch 20/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 9.1208e-04 - val_accuracy: 0.9996\n",
+      "Epoch 21/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 2.6149e-04 - val_accuracy: 0.9998\n",
+      "Epoch 22/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.0011 - val_accuracy: 0.9996\n",
+      "Epoch 23/100\n",
+      "2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 6.3920e-04 - val_accuracy: 0.9996\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x33a122970>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_7lD = get_best_model(tuner_7lD)\n",
+    "model_7lD.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"7_layers_with_dropout\"] = model_7lD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5. Model with 7 layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_7l_builder(hp):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
+    "\n",
+    "    hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
+    "    hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_4 = hp.Int('layer_4', min_value=32, max_value=512, step=32)\n",
+    "    hp_layer_5 = hp.Int('layer_5', min_value=32, max_value=512, step=32)\n",
+    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
+    "\n",
+    "    model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_4, activation=hp_activation))\n",
+    "    model.add(Dense(units=hp_layer_5, activation=hp_activation))\n",
+    "    model.add(Dense(3, activation = \"softmax\"))\n",
+    "\n",
+    "    model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 30 Complete [00h 01m 26s]\n",
+      "accuracy: 0.9993863701820374\n",
+      "\n",
+      "Best accuracy So Far: 0.9993863701820374\n",
+      "Total elapsed time: 00h 17m 16s\n",
+      "INFO:tensorflow:Oracle triggered exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "tuner_7l = kt.Hyperband(\n",
+    "    model_7l_builder,\n",
+    "    objective='accuracy',\n",
+    "    max_epochs=10,\n",
+    "    directory='keras_tuner_dir',\n",
+    "    project_name='keras_tuner_demo_5'\n",
+    ")\n",
+    "tuner_7l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-2: 160 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-4: 288 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-5: 512 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-6: 256 units, func:  <function relu at 0x17f4a2310>\n",
+      "Layer-7: 3 units, func:  <function softmax at 0x17f49d8b0>\n",
+      "\n",
+      "Other params:\n",
+      "learning_rate: 0.0001\n",
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 00:37:07.916975: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - ETA: 0s - loss: 0.0573 - accuracy: 0.9844"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 00:37:36.882232: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0573 - accuracy: 0.9844 - val_loss: 0.0053 - val_accuracy: 0.9982\n",
+      "Epoch 2/100\n",
+      "2282/2282 [==============================] - 33s 15ms/step - loss: 0.0104 - accuracy: 0.9974 - val_loss: 0.0030 - val_accuracy: 0.9991\n",
+      "Epoch 3/100\n",
+      "2282/2282 [==============================] - 35s 15ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
+      "Epoch 4/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0090 - accuracy: 0.9979 - val_loss: 0.0016 - val_accuracy: 0.9995\n",
+      "Epoch 5/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
+      "Epoch 6/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0043 - accuracy: 0.9990 - val_loss: 0.0036 - val_accuracy: 0.9989\n",
+      "Epoch 7/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.0059 - val_accuracy: 0.9982\n",
+      "Epoch 8/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0025 - val_accuracy: 0.9995\n",
+      "Epoch 9/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
+      "Epoch 10/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
+      "Epoch 11/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0038 - val_accuracy: 0.9989\n",
+      "Epoch 12/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
+      "Epoch 13/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
+      "Epoch 14/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 6.6669e-04 - val_accuracy: 0.9998\n",
+      "Epoch 15/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
+      "Epoch 16/100\n",
+      "2282/2282 [==============================] - 34s 15ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9996\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x3086633a0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_7l = get_best_model(tuner_7l)\n",
+    "model_7l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_models[\"7_layers\"] = model_7l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.6. Describe Final Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3_layers: Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-2: 32 units, func:  <function tanh at 0x179e05ee0>\n",
+      "Layer-3: 3 units, func:  <function softmax at 0x179e05160>\n",
+      "\n",
+      "5_layers: Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-2: 96 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-4: 320 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-5: 3 units, func:  <function softmax at 0x179e05160>\n",
+      "\n",
+      "7_layers_with_dropout: Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-2: 480 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-3: 0 units, func: None\n",
+      "Layer-4: 256 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-5: 0 units, func: None\n",
+      "Layer-6: 480 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-7: 3 units, func:  <function softmax at 0x179e05160>\n",
+      "\n",
+      "7_layers: Describe models architecture\n",
+      "Layer-1: 68 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-2: 160 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-3: 416 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-4: 288 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-5: 512 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-6: 256 units, func:  <function relu at 0x179e05b80>\n",
+      "Layer-7: 3 units, func:  <function softmax at 0x179e05160>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, model in final_models.items():\n",
+    "    print(f\"{name}: \", end=\"\")\n",
+    "    describe_model(model)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Evaluate model with Train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[1.0, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 1.0]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1913, 0, 2], [0, 1844, 0], [0, 0, 1945]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[0.999, 1.0, 0.999]</td>\n",
+       "      <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model      Precision Score         Recall Score  \\\n",
+       "0               3_layers    [1.0, 1.0, 0.999]    [0.999, 1.0, 1.0]   \n",
+       "1               5_layers  [0.999, 1.0, 0.999]  [0.999, 1.0, 0.999]   \n",
+       "2  7_layers_with_dropout  [0.999, 1.0, 0.999]  [0.999, 1.0, 0.999]   \n",
+       "3               7_layers  [0.999, 1.0, 0.999]  [0.999, 1.0, 0.999]   \n",
+       "\n",
+       "              F1 score                            Confusion Matrix  \n",
+       "0  [0.999, 1.0, 0.999]  [[1913, 0, 2], [0, 1844, 0], [0, 0, 1945]]  \n",
+       "1  [0.999, 1.0, 0.999]  [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]  \n",
+       "2  [0.999, 1.0, 0.999]  [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]  \n",
+       "3  [0.999, 1.0, 0.999]  [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(x_test, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
+    "    \n",
+    "    train_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "train_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
+    "pd.DataFrame(train_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Evaluation with test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows: 710 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "1    241\n",
+      "2    235\n",
+      "0    234\n",
+      "Name: label, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "test_df = pd.read_csv(\"./test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"H\", \"label\"] = 1\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 2\n",
+    "\n",
+    "print(f'Number of rows: {test_df.shape[0]} \\nNumber of columns: {test_df.shape[1]}\\n')\n",
+    "print(f\"Labels: \\n{test_df['label'].value_counts()}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"]\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>[0.983, 1.0, 1.0]</td>\n",
+       "      <td>[1.0, 0.992, 0.991]</td>\n",
+       "      <td>[0.992, 0.996, 0.996]</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>[0.984, 0.937, 0.883]</td>\n",
+       "      <td>[0.803, 0.992, 0.991]</td>\n",
+       "      <td>[0.885, 0.964, 0.934]</td>\n",
+       "      <td>[[188, 16, 30], [1, 239, 1], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>[0.984, 0.825, 1.0]</td>\n",
+       "      <td>[0.782, 0.996, 0.991]</td>\n",
+       "      <td>[0.871, 0.902, 0.996]</td>\n",
+       "      <td>[[183, 51, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>[0.88, 0.732, 1.0]</td>\n",
+       "      <td>[0.624, 0.996, 0.919]</td>\n",
+       "      <td>[0.73, 0.844, 0.958]</td>\n",
+       "      <td>[[146, 88, 0], [1, 240, 0], [19, 0, 216]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model        Precision Score           Recall Score  \\\n",
+       "0  7_layers_with_dropout      [0.983, 1.0, 1.0]    [1.0, 0.992, 0.991]   \n",
+       "1               5_layers  [0.984, 0.937, 0.883]  [0.803, 0.992, 0.991]   \n",
+       "2               7_layers    [0.984, 0.825, 1.0]  [0.782, 0.996, 0.991]   \n",
+       "3               3_layers     [0.88, 0.732, 1.0]  [0.624, 0.996, 0.919]   \n",
+       "\n",
+       "                F1 score                           Confusion Matrix  \n",
+       "0  [0.992, 0.996, 0.996]    [[234, 0, 0], [2, 239, 0], [2, 0, 233]]  \n",
+       "1  [0.885, 0.964, 0.934]  [[188, 16, 30], [1, 239, 1], [2, 0, 233]]  \n",
+       "2  [0.871, 0.902, 0.996]   [[183, 51, 0], [1, 240, 0], [2, 0, 233]]  \n",
+       "3   [0.73, 0.844, 0.958]  [[146, 88, 0], [1, 240, 0], [19, 0, 216]]  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in final_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
+    "    (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
+    "    \n",
+    "    test_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
+    "\n",
+    "test_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
+    "pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Conclusion & Dumped model\n",
+    "\n",
+    "Best model according to evaluation is model with 7 layers include some dropout layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://72fd27ad-5e1d-4761-a5f0-b2b4682c6a65/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump the best model to a pickle file\n",
+    "with open(\"./model/plank_dp.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models[\"7_layers_with_dropout\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: ram://cc39563d-8dfe-4f23-9dc6-71462ea5ef5b/assets\n",
+      "INFO:tensorflow:Assets written to: ram://5855831e-e2d4-4bb1-9799-a65f926cefb3/assets\n",
+      "INFO:tensorflow:Assets written to: ram://bf30ae68-3e13-4624-8b29-c9c341693ed8/assets\n",
+      "INFO:tensorflow:Assets written to: ram://9b787241-d173-4542-88d3-5551c1cbb80d/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dump final results\n",
+    "with open(\"./model/all_dp.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(final_models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/plank_model/4.evaluation.ipynb b/core/plank_model/4.evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bf676e31b1ddeffcf6a8d875a0e7d5feb8675f45
--- /dev/null
+++ b/core/plank_model/4.evaluation.ipynb
@@ -0,0 +1,881 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
+    "\n",
+    "from yellowbrick.classifier import ROCAUC\n",
+    "\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Set ups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Load models & scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load all sklearn models\n",
+    "with open(\"./model/all_sklearn.pkl\", \"rb\") as f:\n",
+    "    sklearn_models = pickle.load(f)\n",
+    "\n",
+    "# Load all deep learning models\n",
+    "with open(\"./model/all_dp.pkl\", \"rb\") as f:\n",
+    "    dp_models = pickle.load(f)\n",
+    "\n",
+    "# Load input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
+    "    sc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def describe_dataset(dataset_path: str):\n",
+    "    '''\n",
+    "    Describe dataset\n",
+    "    '''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    \n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Process Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
+      "Number of rows: 710 \n",
+      "Number of columns: 69\n",
+      "\n",
+      "Labels: \n",
+      "H    241\n",
+      "L    235\n",
+      "C    234\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load dataset\n",
+    "test_df = describe_dataset(\"./test.csv\")\n",
+    "\n",
+    "# Categorizing label\n",
+    "test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"H\", \"label\"] = 1\n",
+    "test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 2\n",
+    "\n",
+    "# Standard Scaling of features\n",
+    "test_x = test_df.drop(\"label\", axis = 1)\n",
+    "test_x = pd.DataFrame(sc.transform(test_x))\n",
+    "\n",
+    "test_y = test_df[\"label\"].astype('int')\n",
+    "\n",
+    "# # Converting prediction to categorical\n",
+    "test_y_cat = to_categorical(test_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Test set evaluation for all models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Sklearn models evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.995828</td>\n",
+       "      <td>0.995775</td>\n",
+       "      <td>0.995775</td>\n",
+       "      <td>0.995781</td>\n",
+       "      <td>[[234, 0, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.987793</td>\n",
+       "      <td>0.987324</td>\n",
+       "      <td>0.987324</td>\n",
+       "      <td>0.987363</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [7, 0, 228]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.955544</td>\n",
+       "      <td>0.949296</td>\n",
+       "      <td>0.949296</td>\n",
+       "      <td>0.949254</td>\n",
+       "      <td>[[233, 1, 0], [2, 239, 0], [33, 0, 202]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.773783</td>\n",
+       "      <td>0.767606</td>\n",
+       "      <td>0.767606</td>\n",
+       "      <td>0.765410</td>\n",
+       "      <td>[[127, 0, 107], [2, 238, 1], [55, 0, 180]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.981748</td>\n",
+       "      <td>0.981690</td>\n",
+       "      <td>0.981690</td>\n",
+       "      <td>0.981707</td>\n",
+       "      <td>[[228, 6, 0], [3, 237, 1], [3, 0, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.856763</td>\n",
+       "      <td>0.842254</td>\n",
+       "      <td>0.842254</td>\n",
+       "      <td>0.838005</td>\n",
+       "      <td>[[148, 73, 13], [14, 227, 0], [4, 8, 223]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.922452</td>\n",
+       "      <td>0.898592</td>\n",
+       "      <td>0.898592</td>\n",
+       "      <td>0.896179</td>\n",
+       "      <td>[[234, 0, 0], [0, 241, 0], [72, 0, 163]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model  Precision Score  Recall Score  Accuracy Score  F1 Score  \\\n",
+       "0    LR         0.995828      0.995775        0.995775  0.995781   \n",
+       "1   SVC         0.987793      0.987324        0.987324  0.987363   \n",
+       "2   KNN         0.955544      0.949296        0.949296  0.949254   \n",
+       "3   DTC         0.773783      0.767606        0.767606  0.765410   \n",
+       "4  SGDC         0.981748      0.981690        0.981690  0.981707   \n",
+       "5    NB         0.856763      0.842254        0.842254  0.838005   \n",
+       "6    RF         0.922452      0.898592        0.898592  0.896179   \n",
+       "\n",
+       "                             Confusion Matrix  \n",
+       "0     [[234, 0, 0], [1, 240, 0], [2, 0, 233]]  \n",
+       "1     [[234, 0, 0], [2, 239, 0], [7, 0, 228]]  \n",
+       "2    [[233, 1, 0], [2, 239, 0], [33, 0, 202]]  \n",
+       "3  [[127, 0, 107], [2, 238, 1], [55, 0, 180]]  \n",
+       "4     [[228, 6, 0], [3, 237, 1], [3, 0, 232]]  \n",
+       "5  [[148, 73, 13], [14, 227, 0], [4, 8, 223]]  \n",
+       "6    [[234, 0, 0], [0, 241, 0], [72, 0, 163]]  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in sklearn_models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=\"weighted\")\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=\"weighted\")\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=\"weighted\")\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[0, 1, 2])\n",
+    "    testset_final_results.append(( name,  p_score, r_score, a_score, f1_score_result, cm ))\n",
+    "\n",
+    "\n",
+    "sklearn_eval = pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"Accuracy Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "sklearn_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Deep learning models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-28 10:56:43.727144: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-28 10:56:43.847788: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-28 10:56:43.976589: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
+      "2022-11-28 10:56:44.117560: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.869224</td>\n",
+       "      <td>0.847887</td>\n",
+       "      <td>0.847887</td>\n",
+       "      <td>0.843977</td>\n",
+       "      <td>[[146, 88, 0], [1, 240, 0], [19, 0, 216]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.934660</td>\n",
+       "      <td>0.929577</td>\n",
+       "      <td>0.929577</td>\n",
+       "      <td>0.927795</td>\n",
+       "      <td>[[188, 16, 30], [1, 239, 1], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.994461</td>\n",
+       "      <td>0.994366</td>\n",
+       "      <td>0.994366</td>\n",
+       "      <td>0.994378</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.935195</td>\n",
+       "      <td>0.923944</td>\n",
+       "      <td>0.923944</td>\n",
+       "      <td>0.923033</td>\n",
+       "      <td>[[183, 51, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Model  Precision Score  Recall Score  Accuracy Score  \\\n",
+       "0               3_layers         0.869224      0.847887        0.847887   \n",
+       "1               5_layers         0.934660      0.929577        0.929577   \n",
+       "2  7_layers_with_dropout         0.994461      0.994366        0.994366   \n",
+       "3               7_layers         0.935195      0.923944        0.923944   \n",
+       "\n",
+       "   F1 Score                           Confusion Matrix  \n",
+       "0  0.843977  [[146, 88, 0], [1, 240, 0], [19, 0, 216]]  \n",
+       "1  0.927795  [[188, 16, 30], [1, 239, 1], [2, 0, 233]]  \n",
+       "2  0.994378    [[234, 0, 0], [2, 239, 0], [2, 0, 233]]  \n",
+       "3  0.923033   [[183, 51, 0], [1, 240, 0], [2, 0, 233]]  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set_results = []\n",
+    "\n",
+    "for name, model in dp_models.items():\n",
+    "    # Evaluate model\n",
+    "    predict_x = model.predict(test_x, verbose=False) \n",
+    "    y_pred_class = np.argmax(predict_x, axis=1)\n",
+    "    y_test_class = np.argmax(test_y_cat, axis=1)\n",
+    "\n",
+    "    cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
+    "    p_score = precision_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    a_score = accuracy_score(y_test_class, y_pred_class)\n",
+    "    r_score = recall_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    f1_score_result = f1_score(y_test_class, y_pred_class, average=\"weighted\")\n",
+    "    \n",
+    "    test_set_results.append(( name,  p_score, r_score, a_score, f1_score_result, cm ))\n",
+    "\n",
+    "dp_eval = pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"Accuracy Score\", \"F1 Score\", \"Confusion Matrix\"])\n",
+    "\n",
+    "dp_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Final Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.995828</td>\n",
+       "      <td>0.995775</td>\n",
+       "      <td>0.995775</td>\n",
+       "      <td>0.995781</td>\n",
+       "      <td>[[234, 0, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7_layers_with_dropout</td>\n",
+       "      <td>0.994461</td>\n",
+       "      <td>0.994366</td>\n",
+       "      <td>0.994366</td>\n",
+       "      <td>0.994378</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.987793</td>\n",
+       "      <td>0.987324</td>\n",
+       "      <td>0.987324</td>\n",
+       "      <td>0.987363</td>\n",
+       "      <td>[[234, 0, 0], [2, 239, 0], [7, 0, 228]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.981748</td>\n",
+       "      <td>0.981690</td>\n",
+       "      <td>0.981690</td>\n",
+       "      <td>0.981707</td>\n",
+       "      <td>[[228, 6, 0], [3, 237, 1], [3, 0, 232]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.955544</td>\n",
+       "      <td>0.949296</td>\n",
+       "      <td>0.949296</td>\n",
+       "      <td>0.949254</td>\n",
+       "      <td>[[233, 1, 0], [2, 239, 0], [33, 0, 202]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5_layers</td>\n",
+       "      <td>0.934660</td>\n",
+       "      <td>0.929577</td>\n",
+       "      <td>0.929577</td>\n",
+       "      <td>0.927795</td>\n",
+       "      <td>[[188, 16, 30], [1, 239, 1], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7_layers</td>\n",
+       "      <td>0.935195</td>\n",
+       "      <td>0.923944</td>\n",
+       "      <td>0.923944</td>\n",
+       "      <td>0.923033</td>\n",
+       "      <td>[[183, 51, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.922452</td>\n",
+       "      <td>0.898592</td>\n",
+       "      <td>0.898592</td>\n",
+       "      <td>0.896179</td>\n",
+       "      <td>[[234, 0, 0], [0, 241, 0], [72, 0, 163]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3_layers</td>\n",
+       "      <td>0.869224</td>\n",
+       "      <td>0.847887</td>\n",
+       "      <td>0.847887</td>\n",
+       "      <td>0.843977</td>\n",
+       "      <td>[[146, 88, 0], [1, 240, 0], [19, 0, 216]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.856763</td>\n",
+       "      <td>0.842254</td>\n",
+       "      <td>0.842254</td>\n",
+       "      <td>0.838005</td>\n",
+       "      <td>[[148, 73, 13], [14, 227, 0], [4, 8, 223]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.773783</td>\n",
+       "      <td>0.767606</td>\n",
+       "      <td>0.767606</td>\n",
+       "      <td>0.765410</td>\n",
+       "      <td>[[127, 0, 107], [2, 238, 1], [55, 0, 180]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Model  Precision Score  Recall Score  Accuracy Score  \\\n",
+       "0                      LR         0.995828      0.995775        0.995775   \n",
+       "1   7_layers_with_dropout         0.994461      0.994366        0.994366   \n",
+       "2                     SVC         0.987793      0.987324        0.987324   \n",
+       "3                    SGDC         0.981748      0.981690        0.981690   \n",
+       "4                     KNN         0.955544      0.949296        0.949296   \n",
+       "5                5_layers         0.934660      0.929577        0.929577   \n",
+       "6                7_layers         0.935195      0.923944        0.923944   \n",
+       "7                      RF         0.922452      0.898592        0.898592   \n",
+       "8                3_layers         0.869224      0.847887        0.847887   \n",
+       "9                      NB         0.856763      0.842254        0.842254   \n",
+       "10                    DTC         0.773783      0.767606        0.767606   \n",
+       "\n",
+       "    F1 Score                            Confusion Matrix  \n",
+       "0   0.995781     [[234, 0, 0], [1, 240, 0], [2, 0, 233]]  \n",
+       "1   0.994378     [[234, 0, 0], [2, 239, 0], [2, 0, 233]]  \n",
+       "2   0.987363     [[234, 0, 0], [2, 239, 0], [7, 0, 228]]  \n",
+       "3   0.981707     [[228, 6, 0], [3, 237, 1], [3, 0, 232]]  \n",
+       "4   0.949254    [[233, 1, 0], [2, 239, 0], [33, 0, 202]]  \n",
+       "5   0.927795   [[188, 16, 30], [1, 239, 1], [2, 0, 233]]  \n",
+       "6   0.923033    [[183, 51, 0], [1, 240, 0], [2, 0, 233]]  \n",
+       "7   0.896179    [[234, 0, 0], [0, 241, 0], [72, 0, 163]]  \n",
+       "8   0.843977   [[146, 88, 0], [1, 240, 0], [19, 0, 216]]  \n",
+       "9   0.838005  [[148, 73, 13], [14, 227, 0], [4, 8, 223]]  \n",
+       "10  0.765410  [[127, 0, 107], [2, 238, 1], [55, 0, 180]]  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_df = pd.concat([sklearn_eval, dp_eval])\n",
+    "eval_df = eval_df.sort_values(by=['F1 Score'], ascending=False).reset_index(drop=True)\n",
+    "eval_df.to_csv(f\"evaluation.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "eval_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Best model - ROC - Confusion Matrix\n",
+    "\n",
+    "As we can see from the evaluation, the best model according to the F1 Score is the LR model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.98734177, 1.        , 1.        ]),\n",
+       " array([1.        , 0.99585062, 0.99148936]),\n",
+       " array([0.99363057, 0.997921  , 0.9957265 ]))"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model = sklearn_models[\"LR\"]\n",
+    "y_predictions = best_model.predict(test_x)\n",
+    "\n",
+    "p_score = precision_score(test_y, y_predictions, labels=[0, 1, 2], average=None)\n",
+    "r_score = recall_score(test_y, y_predictions, labels=[0, 1, 2], average=None)\n",
+    "f1_score_result = f1_score(test_y, y_predictions, labels=[0, 1, 2], average=None)\n",
+    "\n",
+    "p_score, r_score, f1_score_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.996"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0.994 + 0.998 + 0.996) / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "KNN_cm = eval_df[ eval_df[\"Model\"] == 'LR' ][\"Confusion Matrix\"].values[0]\n",
+    "\n",
+    "cm_array_df = pd.DataFrame(KNN_cm, index=[\"C\", \"H\", \"L\"], columns=[\"C\", \"H\", \"L\"])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6)) \n",
+    "sns.heatmap(cm_array_df, linewidths=1, annot=True, ax=ax, fmt='g', cmap=\"crest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. F1 Score and Confidence correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_labels(y_pred, y_pred_proba, threshold):\n",
+    "    '''Return prediction taking confidence threshold into account'''\n",
+    "    results = []\n",
+    "\n",
+    "    for index, predicted_class in enumerate(y_pred):\n",
+    "        prediction_probabilities = y_pred_proba[index]\n",
+    "        class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
+    "\n",
+    "        results.append(predicted_class if class_prediction_probability >= threshold else -1)\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def calculate_correlation_score_confidence(test_x, test_y):\n",
+    "    '''Calculate correlation between Precision score/Recall score/F1 score and confidence threshold'''\n",
+    "    y_predictions = best_model.predict(test_x)\n",
+    "    y_predict_proba = best_model.predict_proba(test_x)\n",
+    "\n",
+    "    thresholds = list(np.arange(0, 1.01, 0.01))\n",
+    "\n",
+    "    f1_score_results = []\n",
+    "\n",
+    "    for threshold in thresholds:\n",
+    "        true_predictions = to_labels(y_predictions, y_predict_proba, threshold)\n",
+    "        f1_s = list(f1_score(test_y, true_predictions, labels=[0, 1, 2], average=None))\n",
+    "        all_class_f1 = f1_score(test_y, true_predictions, labels=[0, 1, 2], average=\"weighted\")\n",
+    "        f1_s.append(all_class_f1)\n",
+    "        f1_score_results.append(f1_s)\n",
+    "    \n",
+    "    return thresholds, f1_score_results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thresholds, f1_scores = calculate_correlation_score_confidence(test_x, test_y)\n",
+    "\n",
+    "first_class = [ el[0] for el in f1_scores ]\n",
+    "second_class = [ el[1] for el in f1_scores ]\n",
+    "third_class = [ el[2] for el in f1_scores ]\n",
+    "all_classes = [ el[3] for el in f1_scores ]\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "plt.plot(thresholds, first_class, label = \"F1 Score - Correct class\")\n",
+    "plt.plot(thresholds, second_class, label = \"F1 Score - High-back class\")\n",
+    "plt.plot(thresholds, third_class, label = \"F1 Score - Low-back class\")\n",
+    "plt.plot(thresholds, all_classes, label = \"F1 Score - All 3 classes\", linewidth=2.0, color=\"blue\")\n",
+    "plt.legend(loc = 'lower left')\n",
+    "plt.ylim([0.5, 1])\n",
+    "plt.xlim([0.025, 1])\n",
+    "plt.xlabel(\"Thresholds\", fontsize = 12)\n",
+    "plt.ylabel(\"F1 Score\", fontsize = 12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'ROC Curves for LogisticRegression'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualizer = ROCAUC(best_model, classes=[\"Correct\", \"High-back\", \"Low-back\"])\n",
+    "visualizer.target_type_ = \"multiclass\"\n",
+    "visualizer.score(test_x, test_y)        \n",
+    "visualizer.show()                       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/plank_model/5.detection.ipynb b/core/plank_model/5.detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..56e6deea06a9fc07cce7ada3956f8473d6ef285f
--- /dev/null
+++ b/core/plank_model/5.detection.ipynb
@@ -0,0 +1,918 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Detection with the Trained Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[7440]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x110e88860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289efe480). One of the two will be used. Which one is undefined.\n",
+      "objc[7440]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x102fd8a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289efe4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[7440]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x102fd8a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289efe4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[7440]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x102fd8ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289efe520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruct the input structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for plank\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_ELBOW\",\n",
+    "    \"RIGHT_ELBOW\",\n",
+    "    \"LEFT_WRIST\",\n",
+    "    \"RIGHT_WRIST\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\",\n",
+    "    \"LEFT_HEEL\",\n",
+    "    \"RIGHT_HEEL\",\n",
+    "    \"LEFT_FOOT_INDEX\",\n",
+    "    \"RIGHT_FOOT_INDEX\",\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "HEADERS = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup some important functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_important_keypoints(results) -> list:\n",
+    "    '''\n",
+    "    Extract important keypoints from mediapipe pose detection\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "    data = []\n",
+    "    for lm in IMPORTANT_LMS:\n",
+    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "    \n",
+    "    return np.array(data).flatten().tolist()\n",
+    "\n",
+    "\n",
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VIDEO_PATH1 = \"../data/plank/plank_test.mov\"\n",
+    "VIDEO_PATH2 = \"../data/plank/plank_test_1.mp4\"\n",
+    "VIDEO_PATH3 = \"../data/plank/plank_test_2.mp4\"\n",
+    "VIDEO_PATH4 = \"../data/plank/plank_test_3.mp4\"\n",
+    "VIDEO_PATH5 = \"../data/plank/plank_test_4.mp4\"\n",
+    "VIDEO_TEST = \"../../demo/plank.mp4\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Make detection with Scikit learn model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/LR_model.pkl\", \"rb\") as f:\n",
+    "    sklearn_model = pickle.load(f)\n",
+    "\n",
+    "# Dump input scaler\n",
+    "with open(\"./model/input_scaler.pkl\", \"rb\") as f2:\n",
+    "    input_scaler = pickle.load(f2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_PATH5)\n",
+    "current_stage = \"\"\n",
+    "prediction_probability_threshold = 0.6\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            predicted_class = sklearn_model.predict(X)[0]\n",
+    "            prediction_probability = sklearn_model.predict_proba(X)[0]\n",
+    "            # print(predicted_class, prediction_probability)\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if predicted_class == \"C\" and prediction_probability[prediction_probability.argmax()] >= prediction_probability_threshold:\n",
+    "                current_stage = \"Correct\"\n",
+    "            elif predicted_class == \"L\" and prediction_probability[prediction_probability.argmax()] >= prediction_probability_threshold: \n",
+    "                current_stage = \"Low back\"\n",
+    "            elif predicted_class == \"H\" and prediction_probability[prediction_probability.argmax()] >= prediction_probability_threshold: \n",
+    "                current_stage = \"High back\"\n",
+    "            else:\n",
+    "                current_stage = \"unk\"\n",
+    "            \n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (250, 60), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display class\n",
+    "            cv2.putText(image, \"CLASS\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display probability\n",
+    "            cv2.putText(image, \"PROB\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(prediction_probability[np.argmax(prediction_probability)], 2)), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Make detection with Deep Learning Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 00:54:54.408985: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2022-11-23 00:54:54.409101: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model\n",
+    "with open(\"./model/plank_dp.pkl\", \"rb\") as f:\n",
+    "    deep_learning_model = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-23 00:55:24.514124: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
+      "2022-11-23 00:55:24.553362: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
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+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "cap = cv2.VideoCapture(VIDEO_TEST)\n",
+    "current_stage = \"\"\n",
+    "prediction_probability_threshold = 0.6\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 50)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        if not results.pose_landmarks:\n",
+    "            print(\"No human found\")\n",
+    "            continue\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+    "\n",
+    "        # Make detection\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row, ], columns=HEADERS[1:])\n",
+    "            X = pd.DataFrame(input_scaler.transform(X))\n",
+    "            \n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            prediction = deep_learning_model.predict(X)\n",
+    "            predicted_class = np.argmax(prediction, axis=1)[0]\n",
+    "\n",
+    "            prediction_probability = max(prediction.tolist()[0])\n",
+    "            # print(X)\n",
+    "\n",
+    "            # Evaluate model prediction\n",
+    "            if predicted_class == 0 and prediction_probability >= prediction_probability_threshold:\n",
+    "                current_stage = \"Correct\"\n",
+    "            elif predicted_class == 2 and prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"Low back\"\n",
+    "            elif predicted_class == 1 and prediction_probability >= prediction_probability_threshold: \n",
+    "                current_stage = \"High back\"\n",
+    "            else:\n",
+    "                current_stage = \"Unknown\"\n",
+    "\n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (550, 60), (245, 117, 16), -1)\n",
+    "\n",
+    "            # # Display class\n",
+    "            cv2.putText(image, \"DETECTION\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # # Display class\n",
+    "            cv2.putText(image, \"CLASS\", (350, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(predicted_class), (345, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # # Display probability\n",
+    "            cv2.putText(image, \"PROB\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(prediction_probability, 2)), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "        \n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "        \n",
+    "        # Press Q to close cv2 window\n",
+    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/plank_model/README.md b/core/plank_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..14f2b913aa324306aa3d5695628047130ed4ad4b
--- /dev/null
+++ b/core/plank_model/README.md
@@ -0,0 +1,32 @@
+<h2 align="center">BICEP CURL MODEL TRAINING PROCESS</h2>
+
+### 1. Folder structure
+
+```
+plank_model
+│   1.data.ipynb - process collected videos
+|   2.sklearn.ipynb - train models using Sklearn ML algo
+│   3.deep_leaning.ipynb - train models using Deep Learning
+│   4.evaluation.ipynb - evaluate trained models
+│   5.detection.ipynb - detection on test videos
+|   kaggle.csv - data gathered from Kaggle dataset
+|   train.csv - train dataset after converted from videos
+|   test.csv - test dataset after converted from videos
+|   evaluation.csv - models' evaluation results
+│
+└───model/ - folder contains best trained models and input scaler
+│   │
+```
+
+### 2. Important landmarks
+
+There are 3 popular errors of basic plank that will be targeted in this thesis:
+
+-   High lower back: while performing the exercise, instead of keeping the lower back straight, it is raised too high.
+-   Low lower back: while performing the exercise, instead of keeping the lower back straight, it is brought down too low.
+
+In my research and exploration, **_the important MediaPipe Pose landmarks_** for this exercise are: nose, left shoulder, right shoulder, right elbow, left elbow, right wrist, left wrist, right hip, left hip, right knee, left knee, right ankle, left ankle, right heel, left heel, right foot index and left foot index
+
+### 3. Error detection method
+
+Machine learning will be used the errors of this exercise. See this [notebook](./4.evaluation.ipynb) for a evaluation process for this model.
diff --git a/core/plank_model/evaluation.csv b/core/plank_model/evaluation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e0fc67eb2dd556377820cfbd83b0a9f13fe262f2
--- /dev/null
+++ b/core/plank_model/evaluation.csv
@@ -0,0 +1,34 @@
+Model,Precision Score,Recall Score,Accuracy Score,F1 Score,Confusion Matrix
+LR,0.9958281333571047,0.995774647887324,0.995774647887324,0.9957806213166401,"[[234   0   0]
+ [  1 240   0]
+ [  2   0 233]]"
+7_layers_with_dropout,0.9944608829447271,0.9943661971830986,0.9943661971830986,0.9943781816642284,"[[234   0   0]
+ [  2 239   0]
+ [  2   0 233]]"
+SVC,0.9877934272300468,0.9873239436619718,0.9873239436619718,0.9873631313055944,"[[234   0   0]
+ [  2 239   0]
+ [  7   0 228]]"
+SGDC,0.9817476042606512,0.9816901408450704,0.9816901408450704,0.9817072030260251,"[[228   6   0]
+ [  3 237   1]
+ [  3   0 232]]"
+KNN,0.9555438476630931,0.9492957746478873,0.9492957746478873,0.9492535346190375,"[[233   1   0]
+ [  2 239   0]
+ [ 33   0 202]]"
+5_layers,0.9346596362432323,0.9295774647887324,0.9295774647887324,0.9277945444567944,"[[188  16  30]
+ [  1 239   1]
+ [  2   0 233]]"
+7_layers,0.9351953423233113,0.923943661971831,0.923943661971831,0.9230332693416461,"[[183  51   0]
+ [  1 240   0]
+ [  2   0 233]]"
+RF,0.9224523612261807,0.8985915492957747,0.8985915492957747,0.8961794889942669,"[[234   0   0]
+ [  0 241   0]
+ [ 72   0 163]]"
+3_layers,0.8692235103243617,0.847887323943662,0.847887323943662,0.8439772119010872,"[[146  88   0]
+ [  1 240   0]
+ [ 19   0 216]]"
+NB,0.8567629868168859,0.8422535211267606,0.8422535211267606,0.8380048489281261,"[[148  73  13]
+ [ 14 227   0]
+ [  4   8 223]]"
+DTC,0.7737829148805878,0.7676056338028169,0.7676056338028169,0.7654099650785307,"[[127   0 107]
+ [  2 238   1]
+ [ 55   0 180]]"
diff --git a/core/plank_model/kaggle.csv b/core/plank_model/kaggle.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0b3588b40fc5aa4948357eaa5ba0e4ff8bd4bfdb
--- /dev/null
+++ b/core/plank_model/kaggle.csv
@@ -0,0 +1,172 @@
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diff --git a/core/plank_model/train.csv b/core/plank_model/train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8f934200c5829807e0376cc4ef7d4f046406519b
--- /dev/null
+++ b/core/plank_model/train.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67233e953ef909ee5c6cb05a78fdb55e6ff43d2156ac22e2a40f83eb7e87f463
+size 36870514
diff --git a/core/squat_model/1.counter_model.ipynb b/core/squat_model/1.counter_model.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..68a87664d62d2d45f6b5f98948a75208e21ced07
--- /dev/null
+++ b/core/squat_model/1.counter_model.ipynb
@@ -0,0 +1,1096 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[62740]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x111248860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e480). One of the two will be used. Which one is undefined.\n",
+      "objc[62740]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107d00a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[62740]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107d00a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[62740]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x107d00ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x289c9e520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import csv\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "from sklearn.metrics import precision_score, accuracy_score, f1_score, recall_score, confusion_matrix, roc_curve, auc\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Set up important landmarks and functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate Data Frame\n",
+    "\n",
+    "According to my research *the correct form* for a squat is analyzed through the position of:\n",
+    "- Back\n",
+    "- Hip\n",
+    "- Legs\n",
+    "\n",
+    "Therefore, there will be *9 keypoints* which will be extract from mediapipe in order to train or detect a correct form of a squat:\n",
+    "- \"NOSE\",\n",
+    "- \"LEFT_SHOULDER\",\n",
+    "- \"RIGHT_SHOULDER\",\n",
+    "- \"LEFT_HIP\",\n",
+    "- \"RIGHT_HIP\",\n",
+    "- \"LEFT_KNEE\",\n",
+    "- \"RIGHT_KNEE\",\n",
+    "- \"LEFT_ANKLE\",\n",
+    "- \"RIGHT_ANKLE\"\n",
+    "\n",
+    "The data frame will be saved in a .csv file.\n",
+    "\n",
+    "A data frame will contains a \"Label\" columns which represent the label of a data point.\n",
+    "\n",
+    "There are another 9 x 4 columns represent 9 features of a human pose that are important for a squat.\n",
+    "In that each landmark's info will be flatten\n",
+    "\n",
+    "According to the [Mediapipe documentation](https://google.github.io/mediapipe/solutions/pose#python-solution-api),\n",
+    "Each landmark consists of the following:\n",
+    "- x and y: Landmark coordinates normalized to [0.0, 1.0] by the image width and height respectively.\n",
+    "- z: Represents the landmark depth with the depth at the midpoint of hips being the origin, and the smaller the value the closer the landmark is to the camera. The magnitude of z uses roughly the same scale as x.\n",
+    "- visibility: A value in [0.0, 1.0] indicating the likelihood of the landmark being visible (present and not occluded) in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine important landmarks for squat\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\"\n",
+    "]\n",
+    "\n",
+    "# Generate all columns of the data frame\n",
+    "\n",
+    "landmarks = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    landmarks += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
+    "    \n",
+    "\n",
+    "def init_csv(dataset_path: str):\n",
+    "    '''\n",
+    "    Create a blank csv file with just columns\n",
+    "    '''\n",
+    "\n",
+    "    # Write all the columns to a file\n",
+    "    with open(dataset_path, mode=\"w\", newline=\"\") as f:\n",
+    "        csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "        csv_writer.writerow(landmarks)\n",
+    "\n",
+    "        \n",
+    "def export_landmark_to_csv(dataset_path: str, results, action: str) -> None:\n",
+    "    '''\n",
+    "    Export Labeled Data from detected landmark to csv\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "    keypoints = []\n",
+    "\n",
+    "    try:\n",
+    "        # Extract coordinate of important landmarks\n",
+    "        for lm in IMPORTANT_LMS:\n",
+    "            keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "            keypoints.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "        \n",
+    "        keypoints = list(np.array(keypoints).flatten())\n",
+    "\n",
+    "        # Insert action as the label (first column)\n",
+    "        keypoints.insert(0, action)\n",
+    "\n",
+    "        # Append new row to .csv file\n",
+    "        with open(dataset_path, mode=\"a\", newline=\"\") as f:\n",
+    "            csv_writer = csv.writer(f, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
+    "            csv_writer.writerow(keypoints)\n",
+    "        \n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def concat_csv_files_with_same_headers(file_paths: list, saved_path: str):\n",
+    "    '''\n",
+    "    Concat different csv files\n",
+    "    '''\n",
+    "    all_df = []\n",
+    "    for path in file_paths:\n",
+    "        df = pd.read_csv(path, index_col=None, header=0)\n",
+    "        all_df.append(df)\n",
+    "    \n",
+    "    results = pd.concat(all_df, axis=0, ignore_index=True)\n",
+    "    results.to_csv(saved_path, sep=',', encoding='utf-8', index=False)\n",
+    "\n",
+    "\n",
+    "def describe_dataset(dataset_path: str):\n",
+    "    ''''''\n",
+    "\n",
+    "    data = pd.read_csv(dataset_path)\n",
+    "    print(f\"Headers: {list(data.columns.values)}\")\n",
+    "    print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
+    "    print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
+    "    print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
+    "    duplicate = data[data.duplicated()]\n",
+    "    print(f\"Duplicate Rows : {duplicate.sum(axis=1)}\")\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def round_up_metric_results(results) -> list:\n",
+    "    '''Round up metrics results such as precision score, recall score, ...'''\n",
+    "    return list(map(lambda el: round(el, 3), results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Extract data for train set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_PATH = \"./train.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/squat/squat_posture.mp4\")\n",
+    "up_save_count = 0\n",
+    "down_save_count = 0\n",
+    "\n",
+    "# init_csv(DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "        \n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"UP saved: {up_save_count}\", (50, 150), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "        cv2.putText(image, f\"DOWN saved: {down_save_count}\", (50, 200), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "         # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        if k == ord('d'): \n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"down\")\n",
+    "            down_save_count += 1\n",
+    "        elif k == ord(\"u\"):\n",
+    "            export_landmark_to_csv(DATASET_PATH, results, \"up\")\n",
+    "            up_save_count += 1\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v']\n",
+      "Number of rows: 3739 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "down    1914\n",
+      "up      1825\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : Series([], dtype: float64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = describe_dataset(DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Extract data for test set "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TEST_DATASET_PATH = \"./test.csv\"\n",
+    "\n",
+    "cap = cv2.VideoCapture(\"../data/squat/squat_test_4.mp4\")\n",
+    "up_save_count = 0\n",
+    "down_save_count = 0\n",
+    "\n",
+    "# init_csv(TEST_DATASET_PATH)\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "            \n",
+    "        # Reduce size of a frame\n",
+    "        image = rescale_frame(image, 60)\n",
+    "        # image = cv2.flip(image, 1)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        # Display the saved count\n",
+    "        cv2.putText(image, f\"UP saved: {up_save_count}\", (50, 150), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "        cv2.putText(image, f\"DOWN saved: {down_save_count}\", (50, 200), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "         # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        if k == ord('d'): \n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"down\")\n",
+    "            down_save_count += 1\n",
+    "        elif k == ord(\"u\"):\n",
+    "            export_landmark_to_csv(TEST_DATASET_PATH, results, \"up\")\n",
+    "            up_save_count += 1\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Train custom model using Scikit Learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.1 Read and describe data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v']\n",
+      "Number of rows: 4160 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "down    2127\n",
+      "up      2033\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : Series([], dtype: float64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Brief describe of the dataset\n",
+    "df = describe_dataset(\"./train.csv\")\n",
+    "sns.countplot(x='label', data=df, palette=\"Set1\") \n",
+    "df.loc[df[\"label\"] == \"down\", \"label\"] = 0\n",
+    "df.loc[df[\"label\"] == \"up\", \"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract features and class\n",
+    "X = df.drop(\"label\", axis=1) # features\n",
+    "y = df[\"label\"].astype(\"int\")\n",
+    "\n",
+    "sc = StandardScaler()\n",
+    "X = pd.DataFrame(sc.fit_transform(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3319    1\n",
+       "2621    0\n",
+       "3820    1\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Split train set and test set\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
+    "y_test.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.2 Train Machine learning model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>[0.995, 1.0]</td>\n",
+       "      <td>0.997596</td>\n",
+       "      <td>[1.0, 0.995]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[427, 0], [2, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>[0.995, 1.0]</td>\n",
+       "      <td>0.997596</td>\n",
+       "      <td>[1.0, 0.995]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[427, 0], [2, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>[0.995, 1.0]</td>\n",
+       "      <td>0.997596</td>\n",
+       "      <td>[1.0, 0.995]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[427, 0], [2, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>[0.995, 1.0]</td>\n",
+       "      <td>0.997596</td>\n",
+       "      <td>[1.0, 0.995]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[427, 0], [2, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>0.997596</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[0.998, 0.998]</td>\n",
+       "      <td>[[426, 1], [1, 404]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>[0.995, 0.998]</td>\n",
+       "      <td>0.996394</td>\n",
+       "      <td>[0.998, 0.995]</td>\n",
+       "      <td>[0.996, 0.996]</td>\n",
+       "      <td>[[426, 1], [2, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>[0.986, 1.0]</td>\n",
+       "      <td>0.992788</td>\n",
+       "      <td>[1.0, 0.985]</td>\n",
+       "      <td>[0.993, 0.993]</td>\n",
+       "      <td>[[427, 0], [6, 399]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model Precision Score  Accuracy score    Recall Score        F1 score  \\\n",
+       "0    LR    [0.995, 1.0]        0.997596    [1.0, 0.995]  [0.998, 0.998]   \n",
+       "1   SVC    [0.995, 1.0]        0.997596    [1.0, 0.995]  [0.998, 0.998]   \n",
+       "2   KNN    [0.995, 1.0]        0.997596    [1.0, 0.995]  [0.998, 0.998]   \n",
+       "3  SGDC    [0.995, 1.0]        0.997596    [1.0, 0.995]  [0.998, 0.998]   \n",
+       "4    RF  [0.998, 0.998]        0.997596  [0.998, 0.998]  [0.998, 0.998]   \n",
+       "5   DTC  [0.995, 0.998]        0.996394  [0.998, 0.995]  [0.996, 0.996]   \n",
+       "6    NB    [0.986, 1.0]        0.992788    [1.0, 0.985]  [0.993, 0.993]   \n",
+       "\n",
+       "       Confusion Matrix  \n",
+       "0  [[427, 0], [2, 403]]  \n",
+       "1  [[427, 0], [2, 403]]  \n",
+       "2  [[427, 0], [2, 403]]  \n",
+       "3  [[427, 0], [2, 403]]  \n",
+       "4  [[426, 1], [1, 404]]  \n",
+       "5  [[426, 1], [2, 403]]  \n",
+       "6  [[427, 0], [6, 399]]  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algorithms =[(\"LR\", LogisticRegression()),\n",
+    "         (\"SVC\", SVC(probability=True)),\n",
+    "         ('KNN',KNeighborsClassifier()),\n",
+    "         (\"DTC\", DecisionTreeClassifier()),\n",
+    "         (\"SGDC\", CalibratedClassifierCV(SGDClassifier())),\n",
+    "         (\"NB\", GaussianNB()),\n",
+    "         ('RF', RandomForestClassifier()),]\n",
+    "\n",
+    "models = {}\n",
+    "final_results = []\n",
+    "\n",
+    "for name, model in algorithms:\n",
+    "    trained_model = model.fit(X_train, y_train)\n",
+    "    models[name] = trained_model\n",
+    "\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(X_test)\n",
+    "\n",
+    "    p_score = precision_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    a_score = accuracy_score(y_test, model_results)\n",
+    "    r_score = recall_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    f1_score_result = f1_score(y_test, model_results, average=None, labels=[0, 1])\n",
+    "    cm = confusion_matrix(y_test, model_results, labels=[0, 1])\n",
+    "    final_results.append(( name,  round_up_metric_results(p_score), a_score, round_up_metric_results(r_score), round_up_metric_results(f1_score_result), cm))\n",
+    "\n",
+    "# Sort results by F1 score\n",
+    "final_results.sort(key=lambda k: sum(k[4]), reverse=True)\n",
+    "pd.DataFrame(final_results, columns=[\"Model\", \"Precision Score\", \"Accuracy score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3 Test set evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v']\n",
+      "Number of rows: 853 \n",
+      "Number of columns: 37\n",
+      "\n",
+      "Labels: \n",
+      "down    430\n",
+      "up      423\n",
+      "Name: label, dtype: int64\n",
+      "\n",
+      "Missing values: False\n",
+      "\n",
+      "Duplicate Rows : Series([], dtype: float64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = describe_dataset(\"./test.csv\")\n",
+    "test_df.loc[test_df[\"label\"] == \"down\", \"label\"] = 0\n",
+    "test_df.loc[test_df[\"label\"] == \"up\", \"label\"] = 1\n",
+    "\n",
+    "test_x = test_df.drop(\"label\", axis=1)\n",
+    "test_y = test_df[\"label\"].astype(\"int\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Precision score</th>\n",
+       "      <th>Accuracy score</th>\n",
+       "      <th>Recall score</th>\n",
+       "      <th>F1 score</th>\n",
+       "      <th>Confusion Matrix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LR</td>\n",
+       "      <td>0.994141</td>\n",
+       "      <td>0.994138</td>\n",
+       "      <td>0.994138</td>\n",
+       "      <td>0.994138</td>\n",
+       "      <td>[[428, 2], [3, 420]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SGDC</td>\n",
+       "      <td>0.993063</td>\n",
+       "      <td>0.992966</td>\n",
+       "      <td>0.992966</td>\n",
+       "      <td>0.992965</td>\n",
+       "      <td>[[430, 0], [6, 417]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.985207</td>\n",
+       "      <td>0.984760</td>\n",
+       "      <td>0.984760</td>\n",
+       "      <td>0.984754</td>\n",
+       "      <td>[[430, 0], [13, 410]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.977595</td>\n",
+       "      <td>0.976553</td>\n",
+       "      <td>0.976553</td>\n",
+       "      <td>0.976536</td>\n",
+       "      <td>[[430, 0], [20, 403]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DTC</td>\n",
+       "      <td>0.254120</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.337902</td>\n",
+       "      <td>[[430, 0], [423, 0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NB</td>\n",
+       "      <td>0.254120</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.337902</td>\n",
+       "      <td>[[430, 0], [423, 0]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RF</td>\n",
+       "      <td>0.254120</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.504103</td>\n",
+       "      <td>0.337902</td>\n",
+       "      <td>[[430, 0], [423, 0]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Model  Precision score  Accuracy score  Recall score  F1 score  \\\n",
+       "0    LR         0.994141        0.994138      0.994138  0.994138   \n",
+       "1  SGDC         0.993063        0.992966      0.992966  0.992965   \n",
+       "2   KNN         0.985207        0.984760      0.984760  0.984754   \n",
+       "3   SVC         0.977595        0.976553      0.976553  0.976536   \n",
+       "4   DTC         0.254120        0.504103      0.504103  0.337902   \n",
+       "5    NB         0.254120        0.504103      0.504103  0.337902   \n",
+       "6    RF         0.254120        0.504103      0.504103  0.337902   \n",
+       "\n",
+       "        Confusion Matrix  \n",
+       "0   [[428, 2], [3, 420]]  \n",
+       "1   [[430, 0], [6, 417]]  \n",
+       "2  [[430, 0], [13, 410]]  \n",
+       "3  [[430, 0], [20, 403]]  \n",
+       "4   [[430, 0], [423, 0]]  \n",
+       "5   [[430, 0], [423, 0]]  \n",
+       "6   [[430, 0], [423, 0]]  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset_final_results = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    # Evaluate model\n",
+    "    model_results = model.predict(test_x)\n",
+    "\n",
+    "    p_score = precision_score(test_y, model_results, average=\"weighted\")\n",
+    "    a_score = accuracy_score(test_y, model_results)\n",
+    "    r_score = recall_score(test_y, model_results, average=\"weighted\")\n",
+    "    f1_score_result = f1_score(test_y, model_results, average=\"weighted\")\n",
+    "    cm = confusion_matrix(test_y, model_results, labels=[0, 1])\n",
+    "    testset_final_results.append(( name,  (p_score), a_score, (r_score), (f1_score_result), cm ))\n",
+    "\n",
+    "\n",
+    "testset_final_results.sort(key=lambda k: k[4], reverse=True)\n",
+    "eval_df = pd.DataFrame(testset_final_results, columns=[\"Model\", \"Precision score\", \"Accuracy score\", \"Recall score\", \"F1 score\", \"Confusion Matrix\"])\n",
+    "eval_df = eval_df.sort_values(by=['F1 score'], ascending=False).reset_index(drop=True)\n",
+    "eval_df.to_csv(f\"evaluation.csv\", sep=',', encoding='utf-8', index=False)\n",
+    "eval_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3. Dumped model using pickle\n",
+    "\n",
+    "According to the evaluations, there are multiple good models at the moment, therefore, I will pick the Random Forrest model to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/sklearn_models.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/LR_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"LR\"], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./model/SGDC_model.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(models[\"SGDC\"], f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.99303944, 0.99526066]),\n",
+       " array([0.99534884, 0.9929078 ]),\n",
+       " array([0.9941928 , 0.99408284]))"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model = models[\"LR\"]\n",
+    "y_predictions = best_model.predict(test_x)\n",
+    "\n",
+    "p_score = precision_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "r_score = recall_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "f1_score_result = f1_score(test_y, y_predictions, labels=[0, 1], average=None)\n",
+    "\n",
+    "p_score, r_score, f1_score_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "KNN_cm = eval_df[ eval_df[\"Model\"] == 'LR' ][\"Confusion Matrix\"].values[0]\n",
+    "\n",
+    "cm_array_df = pd.DataFrame(KNN_cm, index=[\"down\", \"up\"], columns=[\"down\", \"up\"])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6)) \n",
+    "sns.heatmap(cm_array_df, linewidths=1, annot=True, ax=ax, fmt='g', cmap=\"crest\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_labels(y_pred, y_pred_proba, threshold):\n",
+    "    '''Return prediction taking confidence threshold into account'''\n",
+    "    results = []\n",
+    "\n",
+    "    for index, predicted_class in enumerate(y_pred):\n",
+    "        prediction_probabilities = y_pred_proba[index]\n",
+    "        class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
+    "\n",
+    "        results.append(predicted_class if class_prediction_probability >= threshold else -1)\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def calculate_correlation_score_confidence(test_x, test_y):\n",
+    "    '''Calculate correlation between Precision score/Recall score/F1 score and confidence threshold'''\n",
+    "    y_predictions = best_model.predict(test_x)\n",
+    "    y_predict_proba = best_model.predict_proba(test_x)\n",
+    "\n",
+    "    thresholds = list(np.arange(0, 1.05, 0.01))\n",
+    "\n",
+    "    f1_score_results = []\n",
+    "\n",
+    "    for threshold in thresholds:\n",
+    "        true_predictions = to_labels(y_predictions, y_predict_proba, threshold)\n",
+    "        f1_s = list(f1_score(test_y, true_predictions, labels=[0, 1], average=None))\n",
+    "        all_class_f1 = f1_score(test_y, true_predictions, labels=[0, 1], average=\"weighted\")\n",
+    "        f1_s.append(all_class_f1)\n",
+    "        f1_score_results.append(f1_s)\n",
+    "    \n",
+    "    return thresholds, f1_score_results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thresholds, f1_scores = calculate_correlation_score_confidence(test_x, test_y)\n",
+    "\n",
+    "first_class = [ el[0] for el in f1_scores ]\n",
+    "second_class = [ el[1] for el in f1_scores ]\n",
+    "all_classes = [ el[2] for el in f1_scores ]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "plt.plot(thresholds, first_class, label = \"F1 Score - Correct class\")\n",
+    "plt.plot(thresholds, second_class, label = \"F1 Score - Incorrect class\")\n",
+    "plt.plot(thresholds, all_classes, label = \"F1 Score - All classes\", linewidth=2.0, color=\"blue\")\n",
+    "plt.legend(loc = 'lower left')\n",
+    "plt.ylim([0, 1])\n",
+    "plt.xlim([0.025, 1])\n",
+    "plt.xlabel(\"Thresholds\", fontsize = 12)\n",
+    "plt.ylabel(\"F1 Score\", fontsize = 12)\n",
+    "# plt.axvline(thresholds[np.argmin(abs(precision-recall))], color=\"k\", ls = \"--\")\n",
+    "# plt.title(label = F\"Threshold = {thresholds[np.argmin(abs(precision-recall))]:.3f}\", fontsize = 12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal Threshold: 0.5421684074365937\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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G7sqkiNGAYhERsV9ysjVnTVKStdBldDQ0bWp3VVJEqedGRETsFxJi9dTcfTfExirYyFVRuBEREXts2gSbN5/fHjAAli6Fa6+1rybxCAo3IiJSsIyBiROtxS4feACOHbPaHQ6tDyVuoTE3IiJScP7+G/r0sQYKAzRqZC2lIOJG+o0SEZGCsWED1K9vBRs/P5g6FT75BEJD7a5MPIzCjYiI5K/MTHj9dbjtNti/H2rWhO+/h0GDdBlK8oXCjYiI5C+HA9avh4wMePBBa1K+Bg3srko8mMbciIhI/jDm/CDhOXOsy1G9eqm3RvKdem5ERMS9MjPh5Zehb18r4IB1e3fv3go2UiDUcyMiIu7z55/QsyesXGlt9+4NrVvbW5MUO+q5ERER9/j2W+tuqJUrITDQWs379tvtrkqKIYUbERG5OhkZMGYMtGkDiYlQrx5s2WJdltJlKLGBLkuJiMjV6dkTFi60fu7XD95+G4KC7K1JijX13IiIyNXp399a+PLDD2HWLAUbsZ16bkRExDXp6fDLL3Dzzdb2nXfCH3/ANdfYWpbIOeq5ERGR3DtwAO64A1q2hN9/P9+uYCOFiMKNiIjkzvLl1t1Qa9da2xeGG5FCROFGREQu7+xZGDECOneGo0ehYUPYuhU6dLC7MpEcacyNiIhc2r591npQGzda208+CRMmgL+/vXWJXIbCjYiIXNp771nBJjTUuhPqvvvsrkjkihRuRETk0l58EY4cgX/+E6pVs7sakVzRmBsRETkvPh4ef9waZwPg5wczZijYSJGinhsREbEsXmxNyJeUBGFhMHas3RWJ5Emeem7S09P573//y7vvvsuJEycAOHToECdPnnRrcSIiUgDOnLEGCt9/vxVsmjWzQo5IEeVyz83evXvp0KED+/btIzU1lbZt21KyZElef/11zpw5w4wZM/KjThERyQ+//w7dukFsrLU9YgS89BL4+tpbl8hVcLnn5umnn6ZRo0YcO3aMwMBAZ3vXrl355ptv3FqciIjko+XLrTlrYmOhdGn44gt47TUFGynyXO65WbduHevXr8fPzy9Le5UqVTh48KDbChMRkXxWowZkZlpLKSxYABUr2l2RiFu4HG4yMzPJyMjI1n7gwAFKlizplqJERCSfHD8OpUpZP9eubS2lcOON4KP7S8RzuHxZqm3btkyePNm57XA4OHnyJKNHj6ZTp07urE1ERNzpo4+gShVYvfp8W4MGCjbicVwON5MmTWL16tXUq1ePM2fO0L17d6pWrcrBgwd57bXX8qNGERG5Gikp0K8f9OwJycnWrMMiHszluB4REUFcXBwff/wxMTExZGZm0r9/f3r06JFlgLGIiBQCv/xi3Q21fTs4HDB6NPzrX3ZXJZKvHMYY48oL1qxZQ/PmzfG5qBszPT2dDRs2cNttt7m1QHdLTk4mNDSUpKQkQkJC3HbcU6egRAnr55MnITjYbYcWEXGdMTB3LjzxBJw+DeXKWYOGW7e2uzKRPHHl+9vly1KtW7fm77//ztaelJREa/1PIyJSOHz3nXUp6vRpaNsWfvxRwUaKDZcvSxljcDgc2dqPHj1KsLorREQKh9atoUcPqFcPnnsOvLSUoBQfuQ439957L2DdHdWnTx/8/f2dz2VkZPDTTz/RvHlz91coIiJXZgx8+CF06QLXXGONr/nwQ+u/IsVMrsNNaGgoYPXclCxZMsvgYT8/P5o2bcojjzzi/gpFROTykpPhscfg44+ha1drAUyHQ8FGiq1ch5s5c+YAULVqVYYPH65LUCIihUFsrHU31O+/g7e3teilMQo2Uqy5POZm9OjR+VGHiIi4whiYNg2GDYO0NKhc2eq5adbM7spEbJenaSk//fRTFi1axL59+0hLS8vy3NatW91SmIiIXMLx4zBggHX5CeCuu2DOHLj2WlvLEiksXB4+P2XKFPr27UtYWBixsbE0btyY0qVLs2fPHjp27JgfNYqIyIUyMmDTJmv17kmTYNkyBRuRC7jcczNt2jTee+89HnroIT744ANGjBhB9erVefHFF3Oc/0ZERNzg3HyrDgeULg2ffGLd3n3LLfbWJVIIudxzs2/fPuct34GBgZw4cQKAnj17snDhQvdWJyIi8PffcM891qWnc5o0UbARuQSXw025cuU4evQoAFWqVOH7778HID4+HhdXchARkSvZuNFaufuzz+CZZ6zbvkXkslwON3fccQeff/45AP3792fo0KG0bduWqKgounbt6vYCRUSKpcxMeOMNuO022LcPatSAb74BN66JJ+KpXF44MzMzk8zMTOfCmYsWLWLdunXUrFmTgQMH4ufnly+FuosWzhSRQu/IEejdG5Yvt7ajouC99xRspFhz5fvb5XBzOQcPHqRChQruOly+ULgRkULt5Em4/nqrt8bfH6ZMgUce0aR8Uuzl66rgOUlMTOSpp56iZs2aLr922rRpVKtWjYCAACIjI1m7du1l909NTWXUqFFUqVIFf39/atSowezZs/NauohI4VKihNVrU7u2dbv3o48q2Ii4KNfh5vjx4/To0YOyZcsSERHBlClTyMzM5MUXX6R69ep8//33LoeM6OhohgwZwqhRo4iNjaVly5Z07NiRffv2XfI13bp145tvvmHWrFn89ttvLFy4kDp16rj0viIihcrhw/DHH+e3X3wRtmyBm26yrSSRoizXl6UGDRrE559/TlRUFF999RU7duygffv2nDlzhtGjR9OqVSuX37xJkyY0bNiQ6dOnO9vq1q3LPffcw/jx47Pt/9VXX/Hggw+yZ88ers3lhFWpqamkpqY6t5OTk6lUqZIuS4lI4fDdd9C9O0REwIYN1qUoEckmXy5LffHFF8yZM4cJEybw2WefYYyhVq1afPvtt3kKNmlpacTExNCuXbss7e3atWPDhg05vuazzz6jUaNGvP7661SoUIFatWoxfPhwTp8+fcn3GT9+PKGhoc5HpUqVXK5VRMTtMjJg7Fho0wYSE+HMGasHR0SuWq5nKD506BD16tUDoHr16gQEBDBgwIA8v/GRI0fIyMggPDw8S3t4eDiJiYk5vmbPnj2sW7eOgIAAli5dypEjRxg0aBB///33JS+JjRw5kmHDhjm3z/XciIjYJiEBHn4Yvv3W2u7bF95+W12+Im6S63CTmZmJr6+vc9vb25tgN/yP6LhooJwxJlvbhTU4HA7mz59PaGgoABMnTuT+++9n6tSpBAYGZnuNv78//urmFZHCYuVKK9gcPmyFmenToWdPu6sS8Si5DjfGGPr06eMMCmfOnGHgwIHZAs6SJUtydbwyZcrg7e2drZfm8OHD2XpzzilfvjwVKlRwBhuwxugYYzhw4ADXXXddbj+OiEjBM8YaLHz4MNx4IyxaBLohQsTtcj3mpnfv3oSFhTnHrjz88MNERERkGc9yYei4Ej8/PyIjI1m5cmWW9pUrVzrXrrpYixYtOHToECdPnnS27dy5Ey8vLypWrJjr9xYRsYXDAQsWwNNPww8/KNiI5BO3TuLnqujoaHr27MmMGTNo1qwZ7733HjNnzuSXX36hSpUqjBw5koMHDzJv3jwATp48Sd26dWnatCljx47lyJEjDBgwgFatWjFz5sxcvacm8RORAvXll/Djj/Dcc3ZXIlKkufL9nevLUvkhKiqKo0ePMm7cOBISErjhhhtYvnw5VapUASAhISHLnDclSpRg5cqVPPXUUzRq1IjSpUvTrVs3XnrpJbs+gohIzs6ehX/9C15/3dpu1gzycGepiLjO1p4bO6jnRkTy3b598OCD1oreAE88ARMmQECAvXWJFGFFpudGRMTjfPYZ9OkDx45BaCjMmgX33Wd3VSLFilvWlhIREazLUHffbQWbW26BrVsVbERsoHAjIuIutWtb/x0yBNatg+rVbS1HpLjKU7j58MMPadGiBREREezduxeAyZMn83//939uLU5EpNA7duz8zz17QkwMTJoEfn721SRSzLkcbqZPn86wYcPo1KkTx48fJyMjA4BSpUoxefJkd9cnIlI4pabCU09Zk/H99df59oYN7atJRIA8hJu3336bmTNnMmrUKLy9vZ3tjRo1Ytu2bW4tTkSkUPr9d2jeHN55Bw4ehC++sLsiEbmAy+EmPj6eBg0aZGv39/fn1KlTbilKRKTQWrTI6p3ZuhVKl4b//Me6O0pECg2Xw021atWIi4vL1v7ll186Vw0XEfE4p0/DwIEQFQUnTsCtt0JcHHTubHdlInIRl+e5efbZZ3niiSc4c+YMxhg2bdrEwoULGT9+PO+//35+1CgiYr9x4+Ddd631oUaOhLFjwUdThYkURi7/n9m3b1/S09MZMWIEKSkpdO/enQoVKvDWW2/x4IMP5keNIiL2e+45WL0axoyBdu3srkZELuOqll84cuQImZmZhIWFubOmfKXlF0QkV1JS4IMPrEtRDofVZsz5n0WkQLny/e3ymJuxY8eye/duAMqUKVOkgo2ISK5s3w6NG8OgQTBt2vl2BRuRIsHlcLN48WJq1apF06ZNeeedd/jrwvkdRESKurlzraUTfvkFypWDunXtrkhEXORyuPnpp5/46aefuOOOO5g4cSIVKlSgU6dOLFiwgJSUlPyoUUQk/508Cb17Q9++1iWpNm2su6HuuMPuykTERVc15gZg/fr1LFiwgE8++YQzZ86QnJzsrtryhcbciEg227ZBt27w66/g5WXdGTVypPWziBQKrnx/X/V9jMHBwQQGBuLn58eJEyeu9nAiIgUvKQl27YKICFi4EG67ze6KROQq5OmfJfHx8bz88svUq1ePRo0asXXrVsaMGUNiYqK76xMRyR8Xdlrfeit8/LF1GUrBRqTIc7nnplmzZmzatIkbb7yRvn37Oue5EREpMmJjoV8/mD8fzs2sfv/99tYkIm7jcrhp3bo177//Ptdff31+1CMikn+MgenTYehQSEuDZ56BL7+0uyoRcTOXw80rr7ySH3WIiOSvpCQYMAA+/dTa7tIF5syxtyYRyRe5CjfDhg3j3//+N8HBwQwbNuyy+06cONEthYmIuM2WLdbdUPHx4OsLr70GQ4ZoUj4RD5WrcBMbG8vZs2edP4uIFBkbN0KrVnD2LFStCtHR1uzDIuKxrnqem6JG89yIFDPp6dZEfGXLwqxZUKqU3RWJSB7k69pS/fr1y3E+m1OnTtGvXz9XDyci4n5bt0JqqvWzjw988YU11kbBRqRYcDncfPDBB5w+fTpb++nTp5k3b55bihIRyZPMTJgwAZo0gREjzreXLKnxNSLFSK7vlkpOTsYYgzGGEydOEBAQ4HwuIyOD5cuXa4VwEbHPkSPQp4/VSwPw55+QkQHe3raWJSIFL9fhplSpUjgcDhwOB7Vq1cr2vMPhYOzYsW4tTkQkV9atgwcfhIMHwd8f3noLHn1UvTUixVSuw813332HMYY77riDxYsXc+211zqf8/Pzo0qVKkRERORLkSIiOcrMtG7rfuEFq5emVi1YtAhuvtnuykTERrkON61atQKsdaUqV66MQ/8iEhG7HToEr75qBZsePazZh0uWtLsqEbFZrsLNTz/9xA033ICXlxdJSUls27btkvvedNNNbitOROSyKlaEuXPh2DHo21eXoUQEyGW4qV+/PomJiYSFhVG/fn0cDgc5TY/jcDjIyMhwe5EiIoDVQ/PKK9YkfO3bW21du9pbk4gUOrkKN/Hx8ZQtW9b5s4hIgUtMtC49ffstlCkDO3fCNdfYXZWIFEK5CjdVqlTJ8WcRkQLx3/9awebwYWv674kTFWxE5JLyNInfF+fmkQBGjBhBqVKlaN68OXv37nVrcSJSzKWnW3dCtWtnBZsbb7QWwezZ0+7KRKQQczncvPLKKwQGBgKwceNG3nnnHV5//XXKlCnD0KFD3V6giBRTKSlw553w0ktgjDVvzQ8/QJ06dlcmIoVcrm8FP2f//v3UrFkTgGXLlnH//ffz6KOP0qJFC26//XZ31ycixVVQEFSrZq0TNXOmNUmfiEguuNxzU6JECY4ePQrAihUraNOmDQABAQE5rjklIpJrZ89CUtL57alTITZWwUZEXOJyz03btm0ZMGAADRo0YOfOnXTu3BmAX375hapVq7q7PhEpLvbvt0JMaCj85z/g5WUNHv5fT7GISG653HMzdepUmjVrxl9//cXixYspXbo0ADExMTz00ENuL1BEioHPP4f69WHDBli/3rrNW0Qkjxwmp9n4PFhycjKhoaEkJSUREhLituOeOgUlSlg/nzxp/YNTRK4gLQ1GjrRu7QZo1Aiio6F6dXvrEpFCx5Xvb5cvSwEcP36cWbNmsWPHDhwOB3Xr1qV///6EhobmqWARKYb++AOiomDTJmt7yBBrnSh/fzurEhEP4PJlqS1btlCjRg0mTZrE33//zZEjR5g0aRI1atRg69at+VGjiHgaY+D++61gU6oULFsGkyYp2IiIW7h8Waply5bUrFmTmTNn4uNjdfykp6czYMAA9uzZw5o1a/KlUHfRZSmRQmLLFnjmGZg3DzTzuYhcgSvf3y6Hm8DAQGJjY6lz0URa27dvp1GjRqSkpLhecQFSuBGxye7d1m3d999/vs0YreQtIrniyve3y5elQkJC2LdvX7b2/fv3U7JkSVcPJyLFwSefQMOG1vpQsbHn2xVsRCQfuBxuoqKi6N+/P9HR0ezfv58DBw7w8ccfM2DAAN0KLiJZnTkDgwZBt26QnAyNG0PZsnZXJSIezuW7pSZMmIDD4aBXr16kp6cD4Ovry+OPP86rr77q9gJFpIjaudMKNT/+aPXQjBwJY8eCT55u0hQRybU8z3OTkpLC7t27McZQs2ZNgoKC3F1bvtCYG5ECsGCBtdDlqVNWT81HH1kre4uI5FG+jLlJSUnhiSeeoEKFCoSFhTFgwADKly/PTTfdVGSCjYgUkD/+sILN7bdDXJyCjYgUqFz3D48ePZq5c+fSo0cPAgICWLhwIY8//jiffPJJftYnIkVFZqa1HhTAc89BRAT07Ane3vbWJSLFTq7DzZIlS5g1axYP/m913ocffpgWLVqQkZGBt/7yEinePvgApk+Hb7+FoCAr5PTpY3dVIlJM5fqy1P79+2nZsqVzu3Hjxvj4+HDo0KF8KUxEioBTp6B3byvI/PADvPuu3RWJiOS+5yYjIwM/P7+sL/bxcd4xJSLFzLZt1t1Qv/5q9dSMGweDB9tdlYhI7sONMYY+ffrgf8HaL2fOnGHgwIEEX3Br0JIlS9xboYgULsbArFnw1FPWPDYREbBwIdx2m92ViYgALoSb3r17Z2t7+OGH3VqMiBQBr74Kzz9v/dyxozXeRhPziUghkud5booqzXMjcpUOHIBbboGhQ2H48PN3SImI5KN8XVvK3aZNm0a1atUICAggMjKStWvX5up169evx8fHh/r16+dvgSLFnTGwfv357YoVYdcuGDFCwUZECiVb/2aKjo5myJAhjBo1itjYWFq2bEnHjh1zXJjzQklJSfTq1Ys777yzgCoVKaaSkqxBw7feCv/3f+fbz3VTiogUQraGm4kTJ9K/f38GDBhA3bp1mTx5MpUqVWL69OmXfd1jjz1G9+7dadasWQFVKlIMbdlireT96afg6wsJCXZXJCKSK7aFm7S0NGJiYmh30bTs7dq1Y8OGDZd83Zw5c9i9ezejR4/O1fukpqaSnJyc5SEil2EMvPUWNG8Oe/ZA1aqwbh0MHGh3ZSIiuWJbuDly5AgZGRmEh4dnaQ8PDycxMTHH1+zatYvnnnuO+fPn45PLlYXHjx9PaGio81GpUqWrrl3EYx07BvfeC0OGwNmz1s+xsdC4sd2ViYjkWp7CzYcffkiLFi2IiIhg7969AEyePJn/u/CafC45HI4s28aYbG1gTSLYvXt3xo4dS61atXJ9/JEjR5KUlOR87N+/3+UaRYqNNWtg2TLw84O337YuSZUqZXdVIiIucTncTJ8+nWHDhtGpUyeOHz9ORkYGAKVKlWLy5Mm5Pk6ZMmXw9vbO1ktz+PDhbL05ACdOnGDLli08+eST+Pj44OPjw7hx4/jxxx/x8fHh22+/zfF9/P39CQkJyfIQkUu4+2546SXYsAGefBJy+IeGiEhh53K4efvtt5k5cyajRo3KsmBmo0aN2LZtW66P4+fnR2RkJCtXrszSvnLlSpo3b55t/5CQELZt20ZcXJzzMXDgQGrXrk1cXBxNmjRx9aOIyNGj1rpQFw4WHjUKIiNtK0lE5Grleobic+Lj42nQoEG2dn9/f06dOuXSsYYNG0bPnj1p1KgRzZo147333mPfvn0M/N/AxZEjR3Lw4EHmzZuHl5cXN9xwQ5bXh4WFERAQkK1dRHJh/Xp48EFrUr7Dh2H5crsrEhFxC5fDTbVq1YiLi6NKlSpZ2r/88kvq1avn0rGioqI4evQo48aNIyEhgRtuuIHly5c7j52QkHDFOW9ExEWZmfD66/Cvf0FGBtSqBePH212ViIjbuLz8wpw5c3jhhRd488036d+/P++//z67d+9m/PjxvP/++zz44IP5VatbaPkFKdb++gt69YKvvrK2e/SA6dOhZEl76xIRuQJXvr9d7rnp27cv6enpjBgxgpSUFLp3706FChV46623Cn2wESnWfv4Z2reHQ4cgMBDeeQf69tWgYRHxOFe1cOaRI0fIzMwkLCzMnTXlK/XcSLF18qS14KXDAYsWgcaqiUgRkq89NxcqU6bM1bxcRPLb0aNwzTXWApclSliDhsPClL5FxKPlaUBxTpPsnbNnz56rKkhE3OSbb6wxNcOHWw+AatXsrUlEpAC4HG6GDBmSZfvs2bPExsby1Vdf8eyzz7qrLhHJq4wMGDvWmozPGFiwwFpOIZdLloiIFHUu/2339NNP59g+depUtmzZctUFichVOHQIuneH1aut7UcesRbBVLARkWLEbQtnduzYkcWLF7vrcCLiqq+/hptvtoJNiRJWj81771l3RomIFCNu++fcp59+yrXXXuuuw4mIKxISrHWhUlOhfn2IjrYm5xMRKYZcDjcNGjTIMqDYGENiYiJ//fUX06ZNc2txIpJL5cvDa6/Bzp3w5psQEGB3RSIitnE53Nxzzz1Ztr28vChbtiy33347derUcVddInIlX3wBFSpYPTUAlxgPJyJS3LgUbtLT06latSrt27enXLly+VWTiFxOWho8/7zVQ3PddRATo+UTREQu4NKAYh8fHx5//HFSU1Pzqx4RuZw//oDbbrOCDUDnzuDnZ2tJIiKFjct3SzVp0oTY2Nj8qEVELmfZMmjQAH74AUqVsrYnTQJ/f5sLExEpXFweczNo0CCeeeYZDhw4QGRkJMEXTeN+0003ua04EQHOnrVmGJ4yxdpu2hQ+/hiqVLG3LhGRQirX4aZfv35MnjyZqKgoAAYPHux8zuFwYIzB4XCQkZHh/ipFijMvL9i+3fp5+HB45RXw9bW3JhGRQizXq4J7e3uTkJDA6dOnL7tflUL+r0mtCi5FRmamFWwA/vzTGjjcqZO9NYmI2CRfVgU/l4EKe3gRKfLOnIFhw6w1ot5912oLD1ewERHJJZfG3FxuNXARcYNdu6BbN4iLs7afeAI0jk1ExCUuhZtatWpdMeD8/fffV1WQSLG1cCE8+qh1XbNsWfjwQwUbEZE8cCncjB07ltDQ0PyqRaR4On0aBg+G99+3tm+/HebPh4gIW8sSESmqXAo3Dz74IGFhYflVi0jxY4w1lmbVKnA44IUX4MUXwdvb7spERIqsXIcbjbcRyQcOh3V792+/wUcfwR132F2RiEiR5/LdUiJylU6dgh07oFEja7tzZ2sgseYPEBFxi1wvv5CZmalLUiJX6+ef4ZZboF072Lv3fLuCjYiI27i8tpSI5IExMGsWNG5s9doEBloT84mIiNsp3IjktxMnoGdPGDDAujOqQwdrHpvGje2uTETEIynciOSnuDhrbM38+dYdUK++Cl98Yc1jIyIi+cLlVcFFxAWzZsHOnVCxorWSd4sWdlckIuLxFG5E8tMbb1greI8aBaVL212NiEixoMtSIu4UEwP9+1uLXgIEBMDEiQo2IiIFSOFGxB2MgbffhubNYfZseOstuysSESm2dFlK5GodO2b11ixdam3fcw/07WtrSSIixZl6bkSuxqZN0LChFWz8/GDKFFiyBK65xu7KRESKLfXciOTVvHlWj016OlSvDosWQWSk3VWJiBR76rkRyav69cHHB7p1g61bFWxERAoJ9dyIuOLwYTi3xtpNN1mhpk4da3VvEREpFNRzI5IbmZnw2mtQtSr88MP59rp1FWxERAoZhRuRK/nrL+jcGZ57zlob6tNP7a5IREQuQ5elRC5nzRp46CE4dMiakO+dd6BfP7urEhGRy1DPjUhOMjLgpZegdWsr2NStC5s3W3dH6TKUiEihpnAjkpPFi+GFF6yxNr17W8HmhhvsrkpERHJBl6VEcvLAA7BsGbRvb4UbEREpMtRzIwLWZahJk+DECWvb4YAFCxRsRESKIIUbkUOH4M47YdgwePxxu6sREZGrpHAjxdvXX1szDa9eDSVKQKdOdlckIiJXSeFGiqf0dBg5Ejp0sOaxuflmiImB7t3trkxERK6SBhRL8XPwIERFwfr11vagQfDmm9Y8NiIiUuQp3Ejx4+0Nv/8OISHw/vvWnVEiIuIxFG6keMjIsEINQLlysGQJhIdDjRr21iUiIm6nMTfi+f74A1q0gOjo823NmyvYiIh4KIUb8WzLlkGDBtZK3iNGQFqa3RWJiEg+U7gRz5SWBkOGQNeucPw4NG5s3e7t52d3ZSIiks8UbsTz7NljXYZ66y1r+5lnYO1aqFrV1rJERKRgaECxeJbDh6FhQ0hKgmuvhblzoUsXu6sSEZECpHAjniUsDPr3h++/h48/hkqV7K5IREQKmMKNFH27doG/P1SubG2/+qr1X19f+2oSERHb2D7mZtq0aVSrVo2AgAAiIyNZu3btJfddsmQJbdu2pWzZsoSEhNCsWTO+/vrrAqxWCp2FC63LUA89BGfPWm2+vgo2IiLFmK3hJjo6miFDhjBq1ChiY2Np2bIlHTt2ZN++fTnuv2bNGtq2bcvy5cuJiYmhdevWdOnShdjY2AKuXGx3+jQ8+qi1FtTJk1aYOXHC7qpERKQQcBhjjF1v3qRJExo2bMj06dOdbXXr1uWee+5h/PjxuTrG9ddfT1RUFC+++GKu9k9OTiY0NJSkpCRCQkLyVHdOTp2yFpUG67s2ONhth5aL/fqrtWTCzz+DwwH/+he8+CL46CqriIincuX727aem7S0NGJiYmjXrl2W9nbt2rFhw4ZcHSMzM5MTJ05w7bXXXnKf1NRUkpOTszykCJs3DyIjrWATHg4rVsC4cQo2IiLiZFu4OXLkCBkZGYSHh2dpDw8PJzExMVfHePPNNzl16hTdunW75D7jx48nNDTU+aiku2eKrrQ0a/XulBS4806Ii4M2beyuSkREChnbBxQ7HI4s28aYbG05WbhwIWPGjCE6OpqwsLBL7jdy5EiSkpKcj/379191zWITPz9YtAhefhm+/tpaAFNEROQitvXllylTBm9v72y9NIcPH87Wm3Ox6Oho+vfvzyeffEKbK/zL3d/fH39//6uuV2xgDMyeDUePWutCAdSuDc8/b29dIiJSqNnWc+Pn50dkZCQrV67M0r5y5UqaN29+ydctXLiQPn36sGDBAjp37pzfZYpdTpyAnj1hwAAYORK2brW7IhERKSJsHYU5bNgwevbsSaNGjWjWrBnvvfce+/btY+DAgYB1SengwYPMmzcPsIJNr169eOutt2jatKmz1ycwMJDQ0FDbPoe42Y8/QrdusHMneHvDSy9B/fp2VyUiIkWEreEmKiqKo0ePMm7cOBISErjhhhtYvnw5VapUASAhISHLnDfvvvsu6enpPPHEEzzxxBPO9t69ezN37tyCLl/czRh47z14+mlITYWKFa1J+m691e7KRESkCLF1nhs7aJ6bQqxvX2uhS4B//MP6uXRpOysSEZFCokjMcyOSTdOm1nw1EybAZ58p2IiISJ5o5jOxjzHw55/nb+l+9FG4/XbrjigREZE8Us+N2OPYMbjvPmjWDI4ft9ocDgUbERG5ago3UvB++MFayXvpUjh4ENavt7siERHxIAo3UnCMgYkTrbuf/vgDqleHDRtA8xWJiIgbacyNFIyjR6FPH/jPf6zt+++H998HzU8kIiJupp4bKRjPPWcFG39/mDbNWiNKwUZERPKBem6kYLz6KsTHW7d5a7ZhERHJR+q5kfzx118waZI1zgasOWv++18FGxERyXfquRH3W7MGHnoIDh2yLj3162d3RSIiUoyo50bcJyPDWuSydWsr2NSpA7fcYndVIiJSzKjnRtzjzz/h4YetS08AvXrB1KnnF9wSEREpIAo3cvVWrYIHH7QCTlCQFWr69LG7KhERKaYUbuTqpafD4cNw/fXWLd716tldkYiIFGMKN5I36enWCt4AbdpYSym0bWv13IiIiNhIA4rFdV9/DXXrwu7d59vuvlvBRkRECgWFG8m99HR4/nno0AF+/x3GjbO7IhERkWx0WUpy58ABa+6adeus7YEDrUUwRUREChmFG7myL76A3r2txS9LlrQWvOzWze6qREREcqRwI5f3n/9Aly7Wzw0bQnQ01Kxpb00iIiKXoXAjl9euHTRuDE2awBtvWKt6i4iIFGIKN5Ldd9/BrbeCry/4+cHq1RAQYHdVIiIiuaK7peS8tDQYMgTuuANGjz7frmAjIiJFiHpuxLJnD0RFwZYt1vbZs2AMOBz21iUihU5GRgZnz561uwzxQH5+fnh5XX2/i8KNwKefQv/+kJwM114Lc+eeH0QsIvI/xhgSExM5fvy43aWIh/Ly8qJatWr4+fld1XEUboqzM2fgmWdg2jRru3lzWLgQKle2ty4RKZTOBZuwsDCCgoJwqGdX3CgzM5NDhw6RkJBA5cqVr+r3S+GmONu/Hz74wPr5n/+Ef//bGkQsInKRjIwMZ7ApXbq03eWIhypbtiyHDh0iPT0d36v4PlK4Kc6uuw5mz7Ym5uvY0e5qRKQQOzfGJkhryEk+Onc5KiMj46rCje6WKk5On7aWTViz5nxbt24KNiKSa7oUJfnJXb9f6rkpLn791Qoy27ZZyyns2qVbvEVExCOp56Y4mDcPIiOtYBMWZl2KUrAREREPpXDjyU6dgr59rUUvU1Ksyfni4qBtW7srExEpcBs2bMDb25sOHTpke27VqlU4HI4cb3OvX78+Y8aMydIWGxvLAw88QHh4OAEBAdSqVYtHHnmEnTt35lP1sGbNGrp06UJERAQOh4Nly5bl6nWrV68mMjKSgIAAqlevzowZM7Lts3jxYurVq4e/vz/16tVj6dKlbq6+YCnceKq//7bWhJo7F7y8YOxYWLECype3uzIREVvMnj2bp556inXr1rFv3748H+c///kPTZs2JTU1lfnz57Njxw4+/PBDQkNDeeGFF9xYcVanTp3i5ptv5p133sn1a+Lj4+nUqRMtW7YkNjaW559/nsGDB7N48WLnPhs3biQqKoqePXvy448/0rNnT7p168YPP/yQHx+jQGjMjae65hq4/no4dgwWLIDbb7e7IhHxMMZYncIFLSjI9cnTT506xaJFi9i8eTOJiYnMnTuXF1980eX3TklJoW/fvnTq1ClL70a1atVo0qRJvk5w2LFjRzq6eAPIjBkzqFy5MpMnTwagbt26bNmyhQkTJnDfffcBMHnyZNq2bcvIkSMBGDlyJKtXr2by5MksXLjQrZ+hoKjnxpOcPAlJSdbPDgfMnGldhlKwEZF8kJICJUoU/CMvgSo6OpratWtTu3ZtHn74YebMmYMxxuXjfP311xw5coQRI0bk+HypUqUu+dqBAwdSokSJyz6upkcpJxs3bqRdu3ZZ2tq3b8+WLVuct/dfap8NGza4tZaCpJ4bT/Hjj9bdUDfeCJ98YoWb0FC7qxIRKRRmzZrFww8/DECHDh04efIk33zzDW3atHHpOLt27QKgTp06Ltcwbtw4hg8fftl9IiIiXD7u5SQmJhIeHp6lLTw8nPT0dI4cOUL58uUvuU9iYqJbaylICjdFnTHw3nvw9NOQmmoNIk5IADf/DyIicrGgIKvD2I73dcVvv/3Gpk2bWLJkCQA+Pj5ERUUxe/Zsl8NNXnp7zgkLCyMsLCzPr8+ri+eOOfcZLmzPaZ+iPKeRwk1RlpwMjz4K0dHWdufO1gDiMmVsLUtEigeHA4KD7a7iymbNmkV6ejoVKlRwthlj8PX15dixY1xzzTWEhIQAkJSUlO3S0vHjxwn9X094rVq1APj1119p1qyZS3UMHDiQjz766LL7bN++ncpuXN+vXLly2XpgDh8+jI+Pj3MZjUvtc3FvTlGiMTdF1dat0LChFWx8fOCNN+CzzxRsREQukJ6ezrx583jzzTeJi4tzPn788UeqVKnC/PnzAbjuuuvw8vJi8+bNWV6fkJDAwYMHqV27NgDt2rWjTJkyvP766zm+3+UGFI8bNy5LDTk93H1ZqlmzZqxcuTJL24oVK2jUqJFzeYNL7dO8eXO31lKgTDGTlJRkAJOUlOTW4548aYx1jcj6OV+dPWtMjRrWm1WubMzGjfn8hiJS3J0+fdps377dnD592u5SXLJ06VLj5+dnjh8/nu25559/3tSvX9+5/fjjj5vKlSubpUuXmj179ph169aZVq1amRtvvNGcPXvWud+yZcuMr6+v6dKli1m5cqWJj483mzdvNs8++6yJiorKt89y4sQJExsba2JjYw1gJk6caGJjY83evXud+zz33HOmZ8+ezu09e/aYoKAgM3ToULN9+3Yza9Ys4+vraz799FPnPuvXrzfe3t7m1VdfNTt27DCvvvqq8fHxMd9//32+fZZLudzvmSvf3wo3blKg4cYYY9auNea++4w5erQA3kxEiruiGm7+8Y9/mE6dOuX4XExMjAFMTEyMMcaYM2fOmHHjxpm6deuawMBAU6VKFdOnTx+TkJCQ7bWbN2829957rylbtqzx9/c3NWvWNI8++qjZtWtXvn2W7777zgDZHr1793bu07t3b9OqVassr1u1apVp0KCB8fPzM1WrVjXTp0/PduxPPvnE1K5d2/j6+po6deqYxYsX59vnuBx3hRuHMVcxOqoISk5OJjQ0lKSkJOc1Vnc4dcq6RRGsAXZuvw69aRPs2wf33+/mA4uIXNmZM2eIj4+nWrVqBGj5Fsknl/s9c+X7W2NuCjtjYNIkuPVWaxmF7dvtrkhERKRQ091Shdnff0OfPvD559b2XXfpFm8REZErUM9NYbVhA9SvbwUbPz+YOtWanO8ys1+KiIiIwk3hNGEC3HYb7N8PNWvC99/DoEGuL6YiIiJSDCncFEbHj0NGBjz4IMTEQIMGdlckIiJSZGjMTWGRnm5NxgcwZgxERsI996i3RkRExEXqubFbZia8/LJ1N1RqqtXm4wNduyrYiIiI5IHCjZ3+/BM6dIB//Qt++MEaMCwiIiJXReHGLt9+a90NtXIlBAbC7NnQo4fdVYmIiBR5CjcFLSPDGlPTpg0kJkK9erBlC/Ttq8tQIiIeqGrVqkyePNnuMooVhZuCNmwYjB1rzTzcrx9s3mwFHBERyRd9+vTB4XDgcDjw8fGhcuXKPP744xw7dszu0vJdcnIyL7zwAtdffz2BgYGULl2aW265hddff92jP7/ulipoTz8NixfDq6/Cww/bXY2ISLHQoUMH5syZQ3p6Otu3b6dfv34cP36chQsX2l1avvn777+59dZbSU5O5t///jeRkZH4+fnx+++/s2DBAhYsWMATTzxhd5n5Qj03+S093RpXc0716rB7t4KNiHiOU6cu/ThzJvf7nj595X3zyN/fn3LlylGxYkXatWtHVFQUK1ascD6fkZFB//79qVatGoGBgdSuXZu33noryzH69OnDPffcw4QJEyhfvjylS5fmiSee4OzZs859Dh8+TJcuXQgMDKRatWrMnz8/Wy379u3j7rvvpkSJEoSEhNCtWzf+/PNP5/Njxoyhfv36zJ49m8qVK1OiRAkef/xxMjIyeP311ylXrhxhYWG8/PLLl/3Mzz//PPv27eOHH36gb9++3HTTTdSpU4d//OMfLFiwgEGDBjn3dTgcLFu2LMvrS5Uqxdy5c53bBw8eJCoqimuuuYbSpUtz991388cffzifX7VqFY0bNyY4OJhSpUrRokUL9u7dC8CPP/5I69atKVmyJCEhIURGRrJly5bL1n811HOTnw4cgO7dYd06+OoraNfOavf3t7cuERF3KlHi0s916gRffHF+OywMUlJy3rdVK1i16vx21apw5EjWfYzJa5VOe/bs4auvvsLX19fZlpmZScWKFVm0aBFlypRhw4YNPProo5QvX55u3bo59/vuu+8oX7483333Hb///jtRUVHUr1+fRx55BLAC0P79+/n222/x8/Nj8ODBHD58+ILyDffccw/BwcGsXr2a9PR0Bg0aRFRUFKsu+Oy7d+/myy+/5KuvvmL37t3cf//9xMfHU6tWLVavXs2GDRvo168fd955J02bNs32GTMzM4mOjubhhx+mQoUKOZ4HhwvjPFNSUmjdujUtW7ZkzZo1+Pj48NJLL9GhQwd++uknvLy8uOeee3jkkUdYuHAhaWlpbNq0yfkePXr0oEGDBkyfPh1vb2/i4uKynH+3MzabOnWqqVq1qvH39zcNGzY0a9asuez+q1atMg0bNjT+/v6mWrVqZvr06S69X1JSkgFMUlLS1ZSdzcmTxlj/11k/my++MKZ0aauhZEljlixx6/uJiBSk06dPm+3bt5vTp09nf/LcX345PTp1yrpvUNCl923VKuu+Zcpk3ycPevfubby9vU1wcLAJCAgwgAHMxIkTL/u6QYMGmfvuuy/LcapUqWLS09OdbQ888ICJiooyxhjz22+/GcB8//33zud37NhhADNp0iRjjDErVqww3t7eZt++fc59fvnlFwOYTZs2GWOMGT16tAkKCjLJycnOfdq3b2+qVq1qMjIynG21a9c248ePz7H2xMTEHD9jw4YNTXBwsAkODjYPPvigsx0wS5cuzbJvaGiomTNnjjHGmFmzZpnatWubzMxM5/OpqakmMDDQfP311+bo0aMGMKtWrcqxnpIlS5q5c+fm+NyFLvd75sr3t609N9HR0QwZMoRp06bRokUL3n33XTp27Mj27dupXLlytv3j4+Pp1KkTjzzyCB999BHr169n0KBBlC1blvvuu8+GT5CdD2fx/dcomPyG1dCwIURHW2tEiYh4opMnL/2ct3fW7Qt6MbLxumikxAWXPK5W69atmT59OikpKbz//vvs3LmTp556Kss+M2bM4P3332fv3r2cPn2atLQ06tevn2Wf66+/Hu8LPlP58uXZtm0bADt27MDHx4dGjRo5n69Tpw6lLljweMeOHVSqVIlKlSo52+rVq0epUqXYsWMHt9xyC2DdYVWyZEnnPuHh4Xh7e+N1wTkKDw/P0iuUk4t7Z5YuXUpaWhr//Oc/OX3xZcDLiImJ4ffff89SE8CZM2fYvXs37dq1o0+fPrRv3562bdvSpk0bunXrRvny5QEYNmwYAwYM4MMPP6RNmzY88MAD1KhRI9fv7ypbx9xMnDiR/v37M2DAAOrWrcvkyZOpVKkS06dPz3H/GTNmULlyZSZPnkzdunUZMGAA/fr1Y8KECQVcec4qs5c13IbfuWDz1FPW6t4KNiLiyYKDL/0ICMj9voGBV943zyUGU7NmTW666SamTJlCamoqY8eOdT6/aNEihg4dSr9+/VixYgVxcXH07duXtLS0LMe5+FKKw+EgMzMTsC45nWu7FGNMjs9f3J7T+1zuvS9WtmxZSpUqxa+//pqlvXLlytSsWTNbSHE4HM76z7lwLFFmZiaRkZHExcVleezcuZPu3bsDMGfOHDZu3Ejz5s2Jjo6mVq1afP/994A1juiXX36hc+fOfPvtt9SrV4+lS5fmWLs72BZu0tLSiImJod25cSj/065dOzZs2JDjazZu3Jht//bt27Nly5YsfwgXSk1NJTk5Ocsjv9zGGprxPSY01LojasoUja8RESmERo8ezYQJEzh06BAAa9eupXnz5gwaNIgGDRpQs2ZNdu/e7dIx69atS3p6epaBsr/99hvHjx93bterV499+/axf/9+Z9v27dtJSkqibt26V/ehLuDl5UW3bt346KOPOHjw4BX3L1u2LAkJCc7tXbt2kXLB2KiGDRuya9cuwsLCqFmzZpZHaGioc78GDRowcuRINmzYwA033MCCBQucz9WqVYuhQ4eyYsUK7r33XubMmeOmT5udbeHmyJEjZGRkEB4enqU9PDycxMTEHF+TmJiY4/7p6ekcuXjQ2f+MHz+e0NBQ5+PCrkB3+4iejOQVTq+PhXvvzbf3ERGRq3P77bdz/fXX88orrwBQs2ZNtmzZwtdff83OnTt54YUX2Lx5s0vHrF27Nh06dOCRRx7hhx9+ICYmhgEDBhB4QY9UmzZtuOmmm+jRowdbt25l06ZN9OrVi1atWmW5nOUOr7zyChUqVKBJkybMnj2bn376id27d7N06VI2btyY5fLaHXfcwTvvvMPWrVvZsmULAwcOzNJT1KNHD8qUKcPdd9/N2rVriY+PZ/Xq1Tz99NMcOHCA+Ph4Ro4cycaNG9m7dy8rVqxg586d1K1bl9OnT/Pkk0+yatUq9u7dy/r169m8ebNbw9zFbL8V/OLuuUt12V1u/5zazxk5ciRJSUnOx4Vp2Z2CgqzLzv86OZLAetXy5T1ERMR9hg0bxsyZM9m/fz8DBw7k3nvvJSoqiiZNmnD06NEst0rn1pw5c6hUqRKtWrXi3nvv5dFHHyUsLMz5/Llbrq+55hpuu+022rRpQ/Xq1YmOjnbnRwOgdOnSzvD0xhtv0LhxY2688UbGjBlDVFQUM2fOdO775ptvUqlSJW677Ta6d+/O8OHDCQoKcj4fFBTEmjVrqFy5Mvfeey9169alX79+nD59mpCQEIKCgvj111+57777qFWrFo8++ihPPvkkjz32GN7e3hw9epRevXpRq1YtunXrRseOHbNcFnQ3h7n4IlsBSUtLIygoiE8++YSuXbs6259++mni4uJYvXp1ttfcdtttNGjQIMvcA0uXLqVbt26kpKTk6ray5ORkQkNDSUpKIiQkxD0fRkTEw505c4b4+HiqVatGwMXjaETc5HK/Z658f9vWc+Pn50dkZCQrL5zgDli5ciXNmzfP8TXNmjXLtv+KFSto1KhR/t4vLyIiIkWGrZelhg0bxvvvv8/s2bPZsWMHQ4cOZd++fQwcOBCwLin16tXLuf/AgQPZu3cvw4YNY8eOHcyePZtZs2YxfPhwuz6CiIiIFDK2znMTFRXF0aNHGTduHAkJCdxwww0sX76cKlWqAJCQkMC+ffuc+1erVo3ly5czdOhQpk6dSkREBFOmTCk0c9yIiIiI/Wwbc2MXjbkREXGdxtxIQSjyY25ERKToKWb/HpYC5q7fL4UbERG5onM3baRcatFLETc4NyO098XLdrhIq4KLiMgVeXt7U6pUKedaRkFBQS6tKi1yJZmZmfz1118EBQXh43N18UThRkREcqVcuXIAV1ysUSSvvLy8qFy58lUHZ4UbERHJFYfDQfny5QkLC7vken4iV8PPzy/Lyud5pXAjIiIu8fb2vuoxESL5SQOKRURExKMo3IiIiIhHUbgRERERj1LsxtycmyAoOTnZ5kpEREQkt859b+dmor9iF25OnDgBQKVKlWyuRERERFx14sQJQkNDL7tPsVtbKjMzk0OHDlGyZEm3T0CVnJxMpUqV2L9/v9atykc6zwVD57lg6DwXHJ3rgpFf59kYw4kTJ4iIiLji7eLFrufGy8uLihUr5ut7hISE6H+cAqDzXDB0nguGznPB0bkuGPlxnq/UY3OOBhSLiIiIR1G4EREREY+icONG/v7+jB49Gn9/f7tL8Wg6zwVD57lg6DwXHJ3rglEYznOxG1AsIiIink09NyIiIuJRFG5ERETEoyjciIiIiEdRuBERERGPonDjomnTplGtWjUCAgKIjIxk7dq1l91/9erVREZGEhAQQPXq1ZkxY0YBVVq0uXKelyxZQtu2bSlbtiwhISE0a9aMr7/+ugCrLbpc/X0+Z/369fj4+FC/fv38LdBDuHqeU1NTGTVqFFWqVMHf358aNWowe/bsAqq26HL1PM+fP5+bb76ZoKAgypcvT9++fTl69GgBVVs0rVmzhi5duhAREYHD4WDZsmVXfI0t34NGcu3jjz82vr6+ZubMmWb79u3m6aefNsHBwWbv3r057r9nzx4TFBRknn76abN9+3Yzc+ZM4+vraz799NMCrrxocfU8P/300+a1114zmzZtMjt37jQjR440vr6+ZuvWrQVcedHi6nk+5/jx46Z69eqmXbt25uabby6YYouwvJznu+66yzRp0sSsXLnSxMfHmx9++MGsX7++AKsuelw9z2vXrjVeXl7mrbfeMnv27DFr1641119/vbnnnnsKuPKiZfny5WbUqFFm8eLFBjBLly697P52fQ8q3LigcePGZuDAgVna6tSpY5577rkc9x8xYoSpU6dOlrbHHnvMNG3aNN9q9ASunuec1KtXz4wdO9bdpXmUvJ7nqKgo869//cuMHj1a4SYXXD3PX375pQkNDTVHjx4tiPI8hqvn+Y033jDVq1fP0jZlyhRTsWLFfKvR0+Qm3Nj1PajLUrmUlpZGTEwM7dq1y9Lerl07NmzYkONrNm7cmG3/9u3bs2XLFs6ePZtvtRZleTnPF8vMzOTEiRNce+21+VGiR8jreZ4zZw67d+9m9OjR+V2iR8jLef7ss89o1KgRr7/+OhUqVKBWrVoMHz6c06dPF0TJRVJeznPz5s05cOAAy5cvxxjDn3/+yaeffkrnzp0LouRiw67vwWK3cGZeHTlyhIyMDMLDw7O0h4eHk5iYmONrEhMTc9w/PT2dI0eOUL58+Xyrt6jKy3m+2JtvvsmpU6fo1q1bfpToEfJynnft2sVzzz3H2rVr8fHRXx25kZfzvGfPHtatW0dAQABLly7lyJEjDBo0iL///lvjbi4hL+e5efPmzJ8/n6ioKM6cOUN6ejp33XUXb7/9dkGUXGzY9T2onhsXORyOLNvGmGxtV9o/p3bJytXzfM7ChQsZM2YM0dHRhIWF5Vd5HiO35zkjI4Pu3bszduxYatWqVVDleQxXfp8zMzNxOBzMnz+fxo0b06lTJyZOnMjcuXPVe3MFrpzn7du3M3jwYF588UViYmL46quviI+PZ+DAgQVRarFix/eg/vmVS2XKlMHb2zvbvwIOHz6cLZWeU65cuRz39/HxoXTp0vlWa1GWl/N8TnR0NP379+eTTz6hTZs2+VlmkefqeT5x4gRbtmwhNjaWJ598ErC+hI0x+Pj4sGLFCu64444Cqb0oycvvc/ny5alQoQKhoaHOtrp162KM4cCBA1x33XX5WnNRlJfzPH78eFq0aMGzzz4LwE033URwcDAtW7bkpZdeUs+6m9j1Paiem1zy8/MjMjKSlStXZmlfuXIlzZs3z/E1zZo1y7b/ihUraNSoEb6+vvlWa1GWl/MMVo9Nnz59WLBgga6Z54Kr5zkkJIRt27YRFxfnfAwcOJDatWsTFxdHkyZNCqr0IiUvv88tWrTg0KFDnDx50tm2c+dOvLy8qFixYr7WW1Tl5TynpKTg5ZX1K9Db2xs437MgV8+278F8Ha7sYc7dajhr1iyzfft2M2TIEBMcHGz++OMPY4wxzz33nOnZs6dz/3O3wA0dOtRs377dzJo1S7eC54Kr53nBggXGx8fHTJ061SQkJDgfx48ft+sjFAmunueL6W6p3HH1PJ84ccJUrFjR3H///eaXX34xq1evNtddd50ZMGCAXR+hSHD1PM+ZM8f4+PiYadOmmd27d5t169aZRo0amcaNG9v1EYqEEydOmNjYWBMbG2sAM3HiRBMbG+u85b6wfA8q3Lho6tSppkqVKsbPz880bNjQrF692vlc7969TatWrbLsv2rVKtOgQQPj5+dnqlataqZPn17AFRdNrpznVq1aGSDbo3fv3gVfeBHj6u/zhRRucs/V87xjxw7Tpk0bExgYaCpWrGiGDRtmUlJSCrjqosfV8zxlyhRTr149ExgYaMqXL2969OhhDhw4UMBVFy3ffffdZf++LSzfgw5j1P8mIiIinkNjbkRERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3IiIi4lEUbkQki7lz51KqVCm7y8izqlWrMnny5MvuM2bMGOrXr18g9YhIwVO4EfFAffr0weFwZHv8/vvvdpfG3Llzs9RUvnx5unXrRnx8vFuOv3nzZh599FHntsPhYNmyZVn2GT58ON98841b3u9SLv6c4eHhdOnShV9++cXl4xTlsCliB4UbEQ/VoUMHEhISsjyqVatmd1mAtcp4QkIChw4dYsGCBcTFxXHXXXeRkZFx1ccuW7YsQUFBl92nRIkSlC5d+qrf60ou/JxffPEFp06donPnzqSlpeX7e4sUZwo3Ih7K39+fcuXKZXl4e3szceJEbrzxRoKDg6lUqRKDBg3i5MmTlzzOjz/+SOvWrSlZsiQhISFERkayZcsW5/MbNmzgtttuIzAwkEqVKjF48GBOnTp12docDgflypWjfPnytG7dmtGjR/Pzzz87e5amT59OjRo18PPzo3bt2nz44YdZXj9mzBgqV66Mv78/ERERDB482PnchZelqlatCkDXrl1xOBzO7QsvS3399dcEBARw/PjxLO8xePBgWrVq5bbP2ahRI4YOHcrevXv57bffnPtc7s9j1apV9O3bl6SkJGcP0JgxYwBIS0tjxIgRVKhQgeDgYJo0acKqVasuW49IcaFwI1LMeHl5MWXKFH7++Wc++OADvv32W0aMGHHJ/Xv06EHFihXZvHkzMTExPPfcc/j6+gKwbds22rdvz7333stPP/1EdHQ069at48knn3SppsDAQADOnj3L0qVLefrpp3nmmWf4+eefeeyxx+jbty/fffcdAJ9++imTJk3i3XffZdeuXSxbtowbb7wxx+Nu3rwZgDlz5pCQkODcvlCbNm0oVaoUixcvdrZlZGSwaNEievTo4bbPefz4cRYsWADgPH9w+T+P5s2bM3nyZGcPUEJCAsOHDwegb9++rF+/no8//piffvqJBx54gA4dOrBr165c1yTisfJ93XERKXC9e/c23t7eJjg42Pm4//77c9x30aJFpnTp0s7tOXPmmNDQUOd2yZIlzdy5c3N8bc+ePc2jjz6apW3t2rXGy8vLnD59OsfXXHz8/fv3m6ZNm5qKFSua1NRU07x5c/PII49kec0DDzxgOnXqZIwx5s033zS1atUyaWlpOR6/SpUqZtKkSc5twCxdujTLPqNHjzY333yzc3vw4MHmjjvucG5//fXXxs/Pz/z9999X9TkBExwcbIKCggxgAHPXXXfluP85V/rzMMaY33//3TgcDnPw4MEs7XfeeacZOXLkZY8vUhz42ButRCS/tG7dmunTpzu3g4ODAfjuu+945ZVX2L59O8nJyaSnp3PmzBlOnTrl3OdCw4YNY8CAAXz44Ye0adOGBx54gBo1agAQExPD77//zvz58537G2PIzMwkPj6eunXr5lhbUlISJUqUwBhDSkoKDRs2ZMmSJfj5+bFjx44sA4IBWrRowVtvvQXAAw88wOTJk6levTodOnSgU6dOdOnSBR+fvP911qNHD5o1a8ahQ4eIiIhg/vz5dOrUiWuuueaqPmfJkiXZunUr6enprF69mjfeeIMZM2Zk2cfVPw+ArVu3YoyhVq1aWdpTU1MLZCyRSGGncCPioYKDg6lZs2aWtr1799KpUycGDhzIv//9b6699lrWrVtH//79OXv2bI7HGTNmDN27d+eLL77gyy+/ZPTo0Xz88cd07dqVzMxMHnvssSxjXs6pXLnyJWs796Xv5eVFeHh4ti9xh8ORZdsY42yrVKkSv/32GytXruS///0vgwYN4o033mD16tVZLve4onHjxtSoUYOPP/6Yxx9/nKVLlzJnzhzn83n9nF5eXs4/gzp16pCYmEhUVBRr1qwB8vbnca4eb29vYmJi8Pb2zvJciRIlXPrsIp5I4UakGNmyZQvp6em8+eabeHlZQ+4WLVp0xdfVqlWLWrVqMXToUB566CHmzJlD165dadiwIb/88ku2EHUlF37pX6xu3bqsW7eOXr16Ods2bNiQpXckMDCQu+66i7vuuosnnniCOnXqsG3bNho2bJjteL6+vrm6C6t79+7Mnz+fihUr4uXlRefOnZ3P5fVzXmzo0KFMnDiRpUuX0rVr11z9efj5+WWrv0GDBmRkZHD48GFatmx5VTWJeCINKBYpRmrUqEF6ejpvv/02e/bs4cMPP8x2meRCp0+f5sknn2TVqlXs3buX9evXs3nzZmfQ+Oc//8nGjRt54okniIuLY9euXXz22Wc89dRTea7x2WefZe7cucyYMYNdu3YxceJElixZ4hxIO3fuXGbNmsXPP//s/AyBgYFUqVIlx+NVrVqVb775hsTERI4dO3bJ9+3Rowdbt27l5Zdf5v777ycgIMD5nLs+Z0hICAMGDGD06NEYY3L151G1alVOnjzJN998w5EjR0hJSaFWrVr06NGDXr16sWTJEuLj49m8eTOvvfYay5cvd6kmEY9k54AfEckfvXv3NnfffXeOz02cONGUL1/eBAYGmvbt25t58+YZwBw7dswYk3UAa2pqqnnwwQdNpUqVjJ+fn4mIiDBPPvlklkG0mzZtMm3btjUlSpQwwcHB5qabbjIvv/zyJWvLaYDsxaZNm2aqV69ufH19Ta1atcy8efOczy1dutQ0adLEhISEmODgYNO0aVPz3//+1/n8xQOKP/vsM1OzZk3j4+NjqlSpYozJPqD4nFtuucUA5ttvv832nLs+5969e42Pj4+Jjo42xlz5z8MYYwYOHGhKly5tADN69GhjjDFpaWnmxRdfNFWrVjW+vr6mXLlypmvXruann366ZE0ixYXDGGPsjVciIiIi7qPLUiIiIuJRFG5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2IiIh4FIUbERER8SgKNyIiIuJRFG5ERETEoyjciIiIiEf5f/Dcup3LQN1kAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# calculate the fpr and tpr for all thresholds of the classification\n",
+    "probs = best_model.predict_proba(test_x)\n",
+    "preds = probs[:,1]\n",
+    "fpr, tpr, threshold = roc_curve(test_y, preds)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "optimal_idx = np.argmax(tpr - fpr)\n",
+    "optimal_threshold = threshold[optimal_idx]\n",
+    "print(f\"Optimal Threshold: {optimal_threshold}\")\n",
+    "\n",
+    "# method I: plt\n",
+    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
+    "plt.plot([0, 1], [0, 1],'r--', label=\"Random Guess\")\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/squat_model/2.analyze_bad_pose.ipynb b/core/squat_model/2.analyze_bad_pose.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..94bbafc9f87c48c6c3dafe14a35dab737e336b00
--- /dev/null
+++ b/core/squat_model/2.analyze_bad_pose.ipynb
@@ -0,0 +1,1432 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[78687]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10b908860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece480). One of the two will be used. Which one is undefined.\n",
+      "objc[78687]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece4d0). One of the two will be used. Which one is undefined.\n",
+      "objc[78687]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece4f8). One of the two will be used. Which one is undefined.\n",
+      "objc[78687]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece520). One of the two will be used. Which one is undefined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Drawing helpers\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analyze the width distance\n",
+    "\n",
+    "1. Extract keypoints of shoulder, foot and knee from the dataset\n",
+    "1. Calculate shoulder width, 2 foot width and 2 knees width\n",
+    "1. Calculate the ratio between those widths to figure out the correct feet placement and how much should the knee flare out during the workout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "\n",
+    "\n",
+    "def rescale_frame(frame, percent=50):\n",
+    "    '''\n",
+    "    Rescale a frame to a certain percentage compare to its original frame\n",
+    "    '''\n",
+    "    width = int(frame.shape[1] * percent/ 100)\n",
+    "    height = int(frame.shape[0] * percent/ 100)\n",
+    "    dim = (width, height)\n",
+    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
+    "\n",
+    "\n",
+    "def calculate_distance(pointX, pointY) -> float:\n",
+    "    '''\n",
+    "    Calculate a distance between 2 points\n",
+    "    '''\n",
+    "\n",
+    "    x1, y1 = pointX\n",
+    "    x2, y2 = pointY\n",
+    "\n",
+    "    return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>video</th>\n",
+       "      <th>stage</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [video, stage, shoulder_width, feet_width, knee_width]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns=[\"video\", \"stage\", \"shoulder_width\", \"feet_width\", \"knee_width\"])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Extract data from video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load machine learning model\n",
+    "import pickle\n",
+    "\n",
+    "with open(\"./model/squat_model.pkl\", \"rb\") as f:\n",
+    "    trained_model = pickle.load(f)\n",
+    "\n",
+    "# Prepare for processing input\n",
+    "IMPORTANT_LMS = [\n",
+    "    \"NOSE\",\n",
+    "    \"LEFT_SHOULDER\",\n",
+    "    \"RIGHT_SHOULDER\",\n",
+    "    \"LEFT_HIP\",\n",
+    "    \"RIGHT_HIP\",\n",
+    "    \"LEFT_KNEE\",\n",
+    "    \"RIGHT_KNEE\",\n",
+    "    \"LEFT_ANKLE\",\n",
+    "    \"RIGHT_ANKLE\"\n",
+    "]\n",
+    "\n",
+    "headers = [\"label\"] # Label column\n",
+    "\n",
+    "for lm in IMPORTANT_LMS:\n",
+    "    headers += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]\n",
+    "\n",
+    "\n",
+    "def extract_important_keypoints(results) -> list:\n",
+    "    '''\n",
+    "    Extract important keypoints from mediapipe pose detection\n",
+    "    '''\n",
+    "    landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "    data = []\n",
+    "    for lm in IMPORTANT_LMS:\n",
+    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+    "    \n",
+    "    return np.array(data).flatten().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "video = \"squat_right_4.mp4\"\n",
+    "cap = cv2.VideoCapture(f\"../data/squat/{video}\")\n",
+    "\n",
+    "current_stage = \"\"\n",
+    "prediction_probability_threshold = 0.8\n",
+    "\n",
+    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+    "    while cap.isOpened():\n",
+    "        ret, image = cap.read()\n",
+    "\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "        # Calculate and compare the distance of 2 ankle and shoulder width\n",
+    "        if not results.pose_landmarks:\n",
+    "            continue\n",
+    "        \n",
+    "        # Recolor image from BGR to RGB for mediapipe\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        shoulder_width = feet_width = None\n",
+    "\n",
+    "        try:\n",
+    "            # Extract keypoints from frame for the input\n",
+    "            row = extract_important_keypoints(results)\n",
+    "            X = pd.DataFrame([row], columns=headers[1:])\n",
+    "\n",
+    "            # Make prediction and its probability\n",
+    "            predicted_class = trained_model.predict(X)[0]\n",
+    "            prediction_probability = trained_model.predict_proba(X)[0]\n",
+    "\n",
+    "            # Evaluate model prediction for the current stage\n",
+    "            if prediction_probability[prediction_probability.argmax()] >= prediction_probability_threshold:\n",
+    "                if predicted_class == \"down\":\n",
+    "                    current_stage = \"down\"\n",
+    "                elif current_stage == \"middle\" and predicted_class == \"up\":\n",
+    "                    current_stage = \"up\"\n",
+    "            else:\n",
+    "                current_stage = \"middle\"\n",
+    "            \n",
+    "            # Calculate and compare the distance of 2 ankle and shoulder width\n",
+    "            landmarks = results.pose_landmarks.landmark\n",
+    "\n",
+    "            # * Calculate shoulder width\n",
+    "            left_shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
+    "            right_shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
+    "\n",
+    "            shoulder_width = calculate_distance(left_shoulder, right_shoulder)\n",
+    "\n",
+    "            # * Calculate 2-foot width\n",
+    "            left_foot_index = [landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y]\n",
+    "            right_foot_index = [landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y]\n",
+    "\n",
+    "            feet_width = calculate_distance(left_foot_index, right_foot_index)\n",
+    "            \n",
+    "            # * Calculate 2-knee width\n",
+    "            left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
+    "            right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
+    "\n",
+    "            knee_width = calculate_distance(left_knee, right_knee)\n",
+    "\n",
+    "            # Visualization\n",
+    "            # Status box\n",
+    "            cv2.rectangle(image, (0, 0), (500, 60), (245, 117, 16), -1)\n",
+    "\n",
+    "            # Display feet distance\n",
+    "            cv2.putText(image, \"FEET\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(feet_width, 2)), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display shoulder distance\n",
+    "            cv2.putText(image, \"SHOULDER\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(shoulder_width, 2)), (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display knee distance\n",
+    "            cv2.putText(image, \"KNEE\", (205, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, str(round(knee_width, 2)), (200, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "\n",
+    "            # Display knee distance\n",
+    "            cv2.putText(image, \"STAGE\", (305, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+    "            cv2.putText(image, current_stage, (300, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "\n",
+    "        # Draw landmarks and connections\n",
+    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
+    "\n",
+    "        cv2.imshow(\"CV2\", image)\n",
+    "\n",
+    "        # Pressed key for action\n",
+    "        k = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "        # Press C to add to df\n",
+    "        if k == ord('c'): \n",
+    "            df = df.append({ \"video\": video, \"stage\": current_stage, \"shoulder_width\": shoulder_width, \"feet_width\": feet_width, \"knee_width\": knee_width }, ignore_index=True)\n",
+    "\n",
+    "        # Press q to stop\n",
+    "        elif k == ord(\"q\"):\n",
+    "            break\n",
+    "        else: continue\n",
+    "\n",
+    "    cap.release()\n",
+    "    cv2.destroyAllWindows()\n",
+    "\n",
+    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+    "    for i in range (1, 5):\n",
+    "        cv2.waitKey(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>video</th>\n",
+       "      <th>stage</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079002</td>\n",
+       "      <td>0.104884</td>\n",
+       "      <td>0.099171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079100</td>\n",
+       "      <td>0.104516</td>\n",
+       "      <td>0.102053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079228</td>\n",
+       "      <td>0.103585</td>\n",
+       "      <td>0.104874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079085</td>\n",
+       "      <td>0.103278</td>\n",
+       "      <td>0.109837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079438</td>\n",
+       "      <td>0.102361</td>\n",
+       "      <td>0.110328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>848</th>\n",
+       "      <td>squat_right_4.mp4</td>\n",
+       "      <td>up</td>\n",
+       "      <td>0.087186</td>\n",
+       "      <td>0.147464</td>\n",
+       "      <td>0.112127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>849</th>\n",
+       "      <td>squat_right_4.mp4</td>\n",
+       "      <td>up</td>\n",
+       "      <td>0.089429</td>\n",
+       "      <td>0.153139</td>\n",
+       "      <td>0.109250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>850</th>\n",
+       "      <td>squat_right_4.mp4</td>\n",
+       "      <td>up</td>\n",
+       "      <td>0.090059</td>\n",
+       "      <td>0.152665</td>\n",
+       "      <td>0.104126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>851</th>\n",
+       "      <td>squat_right_4.mp4</td>\n",
+       "      <td>up</td>\n",
+       "      <td>0.089556</td>\n",
+       "      <td>0.154600</td>\n",
+       "      <td>0.097331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>852</th>\n",
+       "      <td>squat_right_4.mp4</td>\n",
+       "      <td>up</td>\n",
+       "      <td>0.089544</td>\n",
+       "      <td>0.158117</td>\n",
+       "      <td>0.094716</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>853 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 video stage  shoulder_width  feet_width  knee_width\n",
+       "0    squat_right_1.mp4  down        0.079002    0.104884    0.099171\n",
+       "1    squat_right_1.mp4  down        0.079100    0.104516    0.102053\n",
+       "2    squat_right_1.mp4  down        0.079228    0.103585    0.104874\n",
+       "3    squat_right_1.mp4  down        0.079085    0.103278    0.109837\n",
+       "4    squat_right_1.mp4  down        0.079438    0.102361    0.110328\n",
+       "..                 ...   ...             ...         ...         ...\n",
+       "848  squat_right_4.mp4    up        0.087186    0.147464    0.112127\n",
+       "849  squat_right_4.mp4    up        0.089429    0.153139    0.109250\n",
+       "850  squat_right_4.mp4    up        0.090059    0.152665    0.104126\n",
+       "851  squat_right_4.mp4    up        0.089556    0.154600    0.097331\n",
+       "852  squat_right_4.mp4    up        0.089544    0.158117    0.094716\n",
+       "\n",
+       "[853 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Analyze the foot placement relate to the shoulder width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>video</th>\n",
+       "      <th>stage</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "      <th>ratio_feet_shoulder</th>\n",
+       "      <th>ratio_knee_feet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079002</td>\n",
+       "      <td>0.104884</td>\n",
+       "      <td>0.099171</td>\n",
+       "      <td>1.327618</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079100</td>\n",
+       "      <td>0.104516</td>\n",
+       "      <td>0.102053</td>\n",
+       "      <td>1.321318</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079228</td>\n",
+       "      <td>0.103585</td>\n",
+       "      <td>0.104874</td>\n",
+       "      <td>1.307429</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079085</td>\n",
+       "      <td>0.103278</td>\n",
+       "      <td>0.109837</td>\n",
+       "      <td>1.305912</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079438</td>\n",
+       "      <td>0.102361</td>\n",
+       "      <td>0.110328</td>\n",
+       "      <td>1.288572</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index              video stage  shoulder_width  feet_width  knee_width  \\\n",
+       "0      0  squat_right_1.mp4  down        0.079002    0.104884    0.099171   \n",
+       "1      1  squat_right_1.mp4  down        0.079100    0.104516    0.102053   \n",
+       "2      2  squat_right_1.mp4  down        0.079228    0.103585    0.104874   \n",
+       "3      3  squat_right_1.mp4  down        0.079085    0.103278    0.109837   \n",
+       "4      4  squat_right_1.mp4  down        0.079438    0.102361    0.110328   \n",
+       "\n",
+       "   ratio_feet_shoulder  ratio_knee_feet  \n",
+       "0             1.327618              0.9  \n",
+       "1             1.321318              1.0  \n",
+       "2             1.307429              1.0  \n",
+       "3             1.305912              1.1  \n",
+       "4             1.288572              1.1  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ratio_feet_shoulder\"] = df[\"feet_width\"] / df[\"shoulder_width\"]\n",
+    "\n",
+    "# Round up the value\n",
+    "# df[\"ratio_feet_shoulder\"] = [round(row, 1) for row in df[\"ratio_feet_shoulder\"]]\n",
+    "\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1. Describe data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Mean value of the ratio between shoulder width and 2 foot: 1.8044653349001174\n",
+      "Max value of the ratio between shoulder width and 2 foot: 2.8\n",
+      "Min value of the ratio between shoulder width and 2 foot: 1.2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = df[\"ratio_feet_shoulder\"].mean(axis=0)\n",
+    "min_val = df[\"ratio_feet_shoulder\"].min()\n",
+    "max_val = df[\"ratio_feet_shoulder\"].max()\n",
+    "\n",
+    "print(\n",
+    "f'''\n",
+    "Mean value of the ratio between shoulder width and 2 foot: {mean}\n",
+    "Max value of the ratio between shoulder width and 2 foot: {max_val}\n",
+    "Min value of the ratio between shoulder width and 2 foot: {min_val}\n",
+    "'''\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2 Conclusion\n",
+    "\n",
+    "According to the analyzed data extracted from the dataset:\n",
+    "\n",
+    "1. Feet distance usually wider compare to shoulder distance\n",
+    "1. The mean ratio between the 2 is 1.8\n",
+    "1. **Conclusion**:\n",
+    "    - Feet distance / shoulder width < 1.2 => **Foot placement is too tight**\n",
+    "    - Feet distance / shoulder width > 2.8 => **Foot placement is too wide**\n",
+    "    - Else: => **Correct form**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Analyze knee position during the workout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>video</th>\n",
+       "      <th>stage</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "      <th>ratio_feet_shoulder</th>\n",
+       "      <th>ratio_knee_feet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079002</td>\n",
+       "      <td>0.104884</td>\n",
+       "      <td>0.099171</td>\n",
+       "      <td>1.327618</td>\n",
+       "      <td>0.945534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079100</td>\n",
+       "      <td>0.104516</td>\n",
+       "      <td>0.102053</td>\n",
+       "      <td>1.321318</td>\n",
+       "      <td>0.976432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079228</td>\n",
+       "      <td>0.103585</td>\n",
+       "      <td>0.104874</td>\n",
+       "      <td>1.307429</td>\n",
+       "      <td>1.012446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079085</td>\n",
+       "      <td>0.103278</td>\n",
+       "      <td>0.109837</td>\n",
+       "      <td>1.305912</td>\n",
+       "      <td>1.063504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>squat_right_1.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079438</td>\n",
+       "      <td>0.102361</td>\n",
+       "      <td>0.110328</td>\n",
+       "      <td>1.288572</td>\n",
+       "      <td>1.077832</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index              video stage  shoulder_width  feet_width  knee_width  \\\n",
+       "0      0  squat_right_1.mp4  down        0.079002    0.104884    0.099171   \n",
+       "1      1  squat_right_1.mp4  down        0.079100    0.104516    0.102053   \n",
+       "2      2  squat_right_1.mp4  down        0.079228    0.103585    0.104874   \n",
+       "3      3  squat_right_1.mp4  down        0.079085    0.103278    0.109837   \n",
+       "4      4  squat_right_1.mp4  down        0.079438    0.102361    0.110328   \n",
+       "\n",
+       "   ratio_feet_shoulder  ratio_knee_feet  \n",
+       "0             1.327618         0.945534  \n",
+       "1             1.321318         0.976432  \n",
+       "2             1.307429         1.012446  \n",
+       "3             1.305912         1.063504  \n",
+       "4             1.288572         1.077832  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ratio_knee_feet\"] = df[\"knee_width\"] / df[\"feet_width\"]\n",
+    "# df[\"ratio_knee_feet\"] = [round(row, 1) for row in df[\"ratio_knee_feet\"]] # round up\n",
+    "\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1 Describe and analyze data\n",
+    "\n",
+    "There are 3 stages of a squat to analyze: UP, MIDDLE, DOWN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Mean value of the ratio between 2 knee width and 2 foot: 0.7131609870740305\n",
+      "Max value of the ratio between 2 knee width and 2 foot: 1.1\n",
+      "Min value of the ratio between 2 knee width and 2 foot: 0.5\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = df[\"ratio_knee_feet\"].mean(axis=0)\n",
+    "min_val = df[\"ratio_knee_feet\"].min()\n",
+    "max_val = df[\"ratio_knee_feet\"].max()\n",
+    "\n",
+    "print(\n",
+    "f'''\n",
+    "Mean value of the ratio between 2 knee width and 2 foot: {mean}\n",
+    "Max value of the ratio between 2 knee width and 2 foot: {max_val}\n",
+    "Min value of the ratio between 2 knee width and 2 foot: {min_val}\n",
+    "'''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**UP position**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Mean value of the ratio between 2 knee width and 2 foot in UP position: 0.6345776031434184\n",
+      "Max value of the ratio between 2 knee width and 2 foot in UP position: 1.0\n",
+      "Min value of the ratio between 2 knee width and 2 foot in UP position: 0.5\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ration_knee_feet = df[ df[\"stage\"] == \"up\" ][\"ratio_knee_feet\"]\n",
+    "\n",
+    "mean = df_ration_knee_feet.mean(axis=0)\n",
+    "min_val = df_ration_knee_feet.min()\n",
+    "max_val = df_ration_knee_feet.max()\n",
+    "\n",
+    "print(\n",
+    "f'''\n",
+    "Mean value of the ratio between 2 knee width and 2 foot in UP position: {mean}\n",
+    "Max value of the ratio between 2 knee width and 2 foot in UP position: {max_val}\n",
+    "Min value of the ratio between 2 knee width and 2 foot in UP position: {min_val}\n",
+    "'''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**MIDDLE position**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Mean value of the ratio between 2 knee width and 2 foot in MIDDLE position: 0.7952830188679246\n",
+      "Max value of the ratio between 2 knee width and 2 foot in MIDDLE position: 1.0\n",
+      "Min value of the ratio between 2 knee width and 2 foot in MIDDLE position: 0.7\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ration_knee_feet = df[ df[\"stage\"] == \"middle\" ][\"ratio_knee_feet\"]\n",
+    "\n",
+    "mean = df_ration_knee_feet.mean(axis=0)\n",
+    "min_val = df_ration_knee_feet.min()\n",
+    "max_val = df_ration_knee_feet.max()\n",
+    "\n",
+    "print(\n",
+    "f'''\n",
+    "Mean value of the ratio between 2 knee width and 2 foot in MIDDLE position: {mean}\n",
+    "Max value of the ratio between 2 knee width and 2 foot in MIDDLE position: {max_val}\n",
+    "Min value of the ratio between 2 knee width and 2 foot in MIDDLE position: {min_val}\n",
+    "'''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**DOWN position**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Mean value of the ratio between 2 knee width and 2 foot in DOWN position: 0.8457627118644067\n",
+      "Max value of the ratio between 2 knee width and 2 foot in DOWN position: 1.1\n",
+      "Min value of the ratio between 2 knee width and 2 foot in DOWN position: 0.7\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ration_knee_feet = df[ df[\"stage\"] == \"down\" ][\"ratio_knee_feet\"]\n",
+    "\n",
+    "mean = df_ration_knee_feet.mean(axis=0)\n",
+    "min_val = df_ration_knee_feet.min()\n",
+    "max_val = df_ration_knee_feet.max()\n",
+    "\n",
+    "print(\n",
+    "f'''\n",
+    "Mean value of the ratio between 2 knee width and 2 foot in DOWN position: {mean}\n",
+    "Max value of the ratio between 2 knee width and 2 foot in DOWN position: {max_val}\n",
+    "Min value of the ratio between 2 knee width and 2 foot in DOWN position: {min_val}\n",
+    "'''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>video</th>\n",
+       "      <th>stage</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "      <th>ratio_feet_shoulder</th>\n",
+       "      <th>ratio_knee_feet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>squat_right_2.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.076213</td>\n",
+       "      <td>0.150768</td>\n",
+       "      <td>0.106627</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>squat_right_2.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.074955</td>\n",
+       "      <td>0.150676</td>\n",
+       "      <td>0.107060</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>squat_right_2.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.074705</td>\n",
+       "      <td>0.149546</td>\n",
+       "      <td>0.108448</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>squat_right_2.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.075373</td>\n",
+       "      <td>0.150687</td>\n",
+       "      <td>0.109482</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>squat_right_2.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.075764</td>\n",
+       "      <td>0.151288</td>\n",
+       "      <td>0.109265</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>squat_right_3.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.082185</td>\n",
+       "      <td>0.219941</td>\n",
+       "      <td>0.155542</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>squat_right_3.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.081883</td>\n",
+       "      <td>0.220649</td>\n",
+       "      <td>0.153821</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>squat_right_3.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.081753</td>\n",
+       "      <td>0.219347</td>\n",
+       "      <td>0.152637</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>squat_right_3.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.080813</td>\n",
+       "      <td>0.214849</td>\n",
+       "      <td>0.151832</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>squat_right_3.mp4</td>\n",
+       "      <td>down</td>\n",
+       "      <td>0.079627</td>\n",
+       "      <td>0.214114</td>\n",
+       "      <td>0.151840</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 video stage  shoulder_width  feet_width  knee_width  \\\n",
+       "180  squat_right_2.mp4  down        0.076213    0.150768    0.106627   \n",
+       "181  squat_right_2.mp4  down        0.074955    0.150676    0.107060   \n",
+       "182  squat_right_2.mp4  down        0.074705    0.149546    0.108448   \n",
+       "183  squat_right_2.mp4  down        0.075373    0.150687    0.109482   \n",
+       "184  squat_right_2.mp4  down        0.075764    0.151288    0.109265   \n",
+       "..                 ...   ...             ...         ...         ...   \n",
+       "643  squat_right_3.mp4  down        0.082185    0.219941    0.155542   \n",
+       "644  squat_right_3.mp4  down        0.081883    0.220649    0.153821   \n",
+       "645  squat_right_3.mp4  down        0.081753    0.219347    0.152637   \n",
+       "646  squat_right_3.mp4  down        0.080813    0.214849    0.151832   \n",
+       "647  squat_right_3.mp4  down        0.079627    0.214114    0.151840   \n",
+       "\n",
+       "     ratio_feet_shoulder  ratio_knee_feet  \n",
+       "180                  2.0              0.7  \n",
+       "181                  2.0              0.7  \n",
+       "182                  2.0              0.7  \n",
+       "183                  2.0              0.7  \n",
+       "184                  2.0              0.7  \n",
+       "..                   ...              ...  \n",
+       "643                  2.7              0.7  \n",
+       "644                  2.7              0.7  \n",
+       "645                  2.7              0.7  \n",
+       "646                  2.7              0.7  \n",
+       "647                  2.7              0.7  \n",
+       "\n",
+       "[84 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = df[ (df[\"ratio_knee_feet\"] == 0.7) & (df[\"stage\"] == \"down\") ]\n",
+    "test\n",
+    "\n",
+    "# df = df[ (df[\"ratio_knee_feet\"] != 0.4) ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Conclusion\n",
+    "\n",
+    "According to the analyzed data extracted from the dataset:\n",
+    "\n",
+    "- In **UP** position:\n",
+    "    1. Knee distance usually tighter compare to foot distance\n",
+    "    1. The mean ratio between knee/foot is 0.63\n",
+    "    1. **Conclusion**:\n",
+    "        - Knee distance / foot width < 0.5 => **2 Knees are too tight**\n",
+    "        - Knee distance / foot width > 1.0 => **2 Knees are too wide**\n",
+    "        - Else: => **Correct form**\n",
+    "\n",
+    "- In **MIDDLE** position:\n",
+    "    1. Knee distance usually equals compare to foot distance\n",
+    "    1. The mean ratio between knee/foot is 0.8\n",
+    "    1. **Conclusion**:\n",
+    "        - Knee distance / foot width < 0.7 => **2 Knees are too tight**\n",
+    "        - Knee distance / foot width > 1.0 => **2 Knees are too wide**\n",
+    "        - Else: => **Correct form**\n",
+    "\n",
+    "- In **DOWN** position:\n",
+    "    1. Knee distance usually equals or a little bit wider compare to foot distance\n",
+    "    1. The mean ratio between knee/foot is 0.8\n",
+    "    1. **Conclusion**:\n",
+    "        - Knee distance / foot width < 0.7 => **2 Knees are too tight**\n",
+    "        - Knee distance / foot width > 1.1 => **2 Knees are too wide**\n",
+    "        - Else: => **Correct form**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Save Analysis CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"./analyze_pose.csv\", sep=',', encoding='utf-8', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"./analyze_pose.csv\")\n",
+    "df = df.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>shoulder_width</th>\n",
+       "      <th>feet_width</th>\n",
+       "      <th>knee_width</th>\n",
+       "      <th>ratio_feet_shoulder</th>\n",
+       "      <th>ratio_knee_feet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>851.000000</td>\n",
+       "      <td>851.000000</td>\n",
+       "      <td>851.000000</td>\n",
+       "      <td>851.000000</td>\n",
+       "      <td>851.000000</td>\n",
+       "      <td>851.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>425.000000</td>\n",
+       "      <td>0.082446</td>\n",
+       "      <td>0.148388</td>\n",
+       "      <td>0.103947</td>\n",
+       "      <td>1.804465</td>\n",
+       "      <td>0.713161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>245.806835</td>\n",
+       "      <td>0.007008</td>\n",
+       "      <td>0.026802</td>\n",
+       "      <td>0.021360</td>\n",
+       "      <td>0.322933</td>\n",
+       "      <td>0.148453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.073848</td>\n",
+       "      <td>0.098033</td>\n",
+       "      <td>0.063072</td>\n",
+       "      <td>1.200000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>212.500000</td>\n",
+       "      <td>0.077047</td>\n",
+       "      <td>0.147486</td>\n",
+       "      <td>0.087311</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>425.000000</td>\n",
+       "      <td>0.079676</td>\n",
+       "      <td>0.151806</td>\n",
+       "      <td>0.101141</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>637.500000</td>\n",
+       "      <td>0.089282</td>\n",
+       "      <td>0.155863</td>\n",
+       "      <td>0.115419</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>850.000000</td>\n",
+       "      <td>0.099635</td>\n",
+       "      <td>0.220649</td>\n",
+       "      <td>0.158517</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.100000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            index  shoulder_width  feet_width  knee_width  \\\n",
+       "count  851.000000      851.000000  851.000000  851.000000   \n",
+       "mean   425.000000        0.082446    0.148388    0.103947   \n",
+       "std    245.806835        0.007008    0.026802    0.021360   \n",
+       "min      0.000000        0.073848    0.098033    0.063072   \n",
+       "25%    212.500000        0.077047    0.147486    0.087311   \n",
+       "50%    425.000000        0.079676    0.151806    0.101141   \n",
+       "75%    637.500000        0.089282    0.155863    0.115419   \n",
+       "max    850.000000        0.099635    0.220649    0.158517   \n",
+       "\n",
+       "       ratio_feet_shoulder  ratio_knee_feet  \n",
+       "count           851.000000       851.000000  \n",
+       "mean              1.804465         0.713161  \n",
+       "std               0.322933         0.148453  \n",
+       "min               1.200000         0.500000  \n",
+       "25%               1.600000         0.600000  \n",
+       "50%               1.900000         0.700000  \n",
+       "75%               2.000000         0.800000  \n",
+       "max               2.800000         1.100000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='ratio_feet_shoulder'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Theme:\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "# Creating Strip plot for day-wise revenue:\n",
+    "sns.swarmplot(y=\"ratio_feet_shoulder\", data=df, palette=\"viridis\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='stage', ylabel='ratio_knee_feet'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Theme:\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "# Creating Strip plot for day-wise revenue:\n",
+    "sns.swarmplot(x='stage', y=\"ratio_knee_feet\", data=df, palette=\"viridis\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 (conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/core/squat_model/3.detection.ipynb b/core/squat_model/3.detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2f7d95056f8fb583e8f99c5851d9d1bf36f15460
--- /dev/null
+++ b/core/squat_model/3.detection.ipynb
@@ -0,0 +1,407 @@
+{
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "objc[24504]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x1153c8860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f6480). One of the two will be used. Which one is undefined.\n",
+                        "objc[24504]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f64d0). One of the two will be used. Which one is undefined.\n",
+                        "objc[24504]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f64f8). One of the two will be used. Which one is undefined.\n",
+                        "objc[24504]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f6520). One of the two will be used. Which one is undefined.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import mediapipe as mp\n",
+                "import cv2\n",
+                "import numpy as np\n",
+                "import pandas as pd\n",
+                "import pickle\n",
+                "\n",
+                "# Drawing helpers\n",
+                "mp_drawing = mp.solutions.drawing_utils\n",
+                "mp_pose = mp.solutions.pose"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 1. Set up important functions and variables"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "IMPORTANT_LMS = [\n",
+                "    \"NOSE\",\n",
+                "    \"LEFT_SHOULDER\",\n",
+                "    \"RIGHT_SHOULDER\",\n",
+                "    \"LEFT_HIP\",\n",
+                "    \"RIGHT_HIP\",\n",
+                "    \"LEFT_KNEE\",\n",
+                "    \"RIGHT_KNEE\",\n",
+                "    \"LEFT_ANKLE\",\n",
+                "    \"RIGHT_ANKLE\"\n",
+                "]\n",
+                "\n",
+                "headers = [\"label\"] # Label column\n",
+                "\n",
+                "for lm in IMPORTANT_LMS:\n",
+                "    headers += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]\n",
+                "\n",
+                "\n",
+                "def extract_important_keypoints(results) -> list:\n",
+                "    '''\n",
+                "    Extract important keypoints from mediapipe pose detection\n",
+                "    '''\n",
+                "    landmarks = results.pose_landmarks.landmark\n",
+                "\n",
+                "    data = []\n",
+                "    for lm in IMPORTANT_LMS:\n",
+                "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
+                "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
+                "    \n",
+                "    return np.array(data).flatten().tolist()\n",
+                "\n",
+                "\n",
+                "def rescale_frame(frame, percent=50):\n",
+                "    '''\n",
+                "    Rescale a frame to a certain percentage compare to its original frame\n",
+                "    '''\n",
+                "    width = int(frame.shape[1] * percent/ 100)\n",
+                "    height = int(frame.shape[0] * percent/ 100)\n",
+                "    dim = (width, height)\n",
+                "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 2. Analyze and detection bad pose\n",
+                "\n",
+                "Look through [this](./analyze_bad_pose.ipynb) on how we analyze bad foot and knee placement while doing squat."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import math \n",
+                "\n",
+                "\n",
+                "def calculate_distance(pointX, pointY) -> float:\n",
+                "    '''\n",
+                "    Calculate a distance between 2 points\n",
+                "    '''\n",
+                "\n",
+                "    x1, y1 = pointX\n",
+                "    x2, y2 = pointY\n",
+                "\n",
+                "    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n",
+                "\n",
+                "\n",
+                "def analyze_foot_knee_placement(results, stage: str, foot_shoulder_ratio_thresholds: list, knee_foot_ratio_thresholds: dict, visibility_threshold: int) -> dict:\n",
+                "    '''\n",
+                "    Calculate the ratio between the foot and shoulder for FOOT PLACEMENT analysis\n",
+                "    \n",
+                "    Calculate the ratio between the knee and foot for KNEE PLACEMENT analysis\n",
+                "\n",
+                "    Return result explanation:\n",
+                "        -1: Unknown result due to poor visibility\n",
+                "        0: Correct knee placement\n",
+                "        1: Placement too tight\n",
+                "        2: Placement too wide\n",
+                "    '''\n",
+                "    analyzed_results = {\n",
+                "        \"foot_placement\": -1,\n",
+                "        \"knee_placement\": -1,\n",
+                "    }\n",
+                "\n",
+                "    landmarks = results.pose_landmarks.landmark\n",
+                "\n",
+                "    # * Visibility check of important landmarks for foot placement analysis\n",
+                "    left_foot_index_vis = landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].visibility\n",
+                "    right_foot_index_vis = landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].visibility\n",
+                "\n",
+                "    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility\n",
+                "    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility\n",
+                "\n",
+                "    # If visibility of any keypoints is low cancel the analysis\n",
+                "    if (left_foot_index_vis < visibility_threshold or right_foot_index_vis < visibility_threshold or left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold):\n",
+                "        return analyzed_results\n",
+                "    \n",
+                "    # * Calculate shoulder width\n",
+                "    left_shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
+                "    right_shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
+                "    shoulder_width = calculate_distance(left_shoulder, right_shoulder)\n",
+                "\n",
+                "    # * Calculate 2-foot width\n",
+                "    left_foot_index = [landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y]\n",
+                "    right_foot_index = [landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y]\n",
+                "    foot_width = calculate_distance(left_foot_index, right_foot_index)\n",
+                "\n",
+                "    # * Calculate foot and shoulder ratio\n",
+                "    foot_shoulder_ratio = round(foot_width / shoulder_width, 1)\n",
+                "\n",
+                "    # * Analyze FOOT PLACEMENT\n",
+                "    min_ratio_foot_shoulder, max_ratio_foot_shoulder = foot_shoulder_ratio_thresholds\n",
+                "    if min_ratio_foot_shoulder <= foot_shoulder_ratio <= max_ratio_foot_shoulder:\n",
+                "        analyzed_results[\"foot_placement\"] = 0\n",
+                "    elif foot_shoulder_ratio < min_ratio_foot_shoulder:\n",
+                "        analyzed_results[\"foot_placement\"] = 1\n",
+                "    elif foot_shoulder_ratio > max_ratio_foot_shoulder:\n",
+                "        analyzed_results[\"foot_placement\"] = 2\n",
+                "    \n",
+                "    # * Visibility check of important landmarks for knee placement analysis\n",
+                "    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility\n",
+                "    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility\n",
+                "\n",
+                "    # If visibility of any keypoints is low cancel the analysis\n",
+                "    if (left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold):\n",
+                "        print(\"Cannot see foot\")\n",
+                "        return analyzed_results\n",
+                "\n",
+                "    # * Calculate 2 knee width\n",
+                "    left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
+                "    right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
+                "    knee_width = calculate_distance(left_knee, right_knee)\n",
+                "\n",
+                "    # * Calculate foot and shoulder ratio\n",
+                "    knee_foot_ratio = round(knee_width / foot_width, 1)\n",
+                "\n",
+                "    # * Analyze KNEE placement\n",
+                "    up_min_ratio_knee_foot, up_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"up\")\n",
+                "    middle_min_ratio_knee_foot, middle_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"middle\")\n",
+                "    down_min_ratio_knee_foot, down_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"down\")\n",
+                "\n",
+                "    if stage == \"up\":\n",
+                "        if up_min_ratio_knee_foot <= knee_foot_ratio <= up_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 0\n",
+                "        elif knee_foot_ratio < up_min_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 1\n",
+                "        elif knee_foot_ratio > up_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 2\n",
+                "    elif stage == \"middle\":\n",
+                "        if middle_min_ratio_knee_foot <= knee_foot_ratio <= middle_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 0\n",
+                "        elif knee_foot_ratio < middle_min_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 1\n",
+                "        elif knee_foot_ratio > middle_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 2\n",
+                "    elif stage == \"down\":\n",
+                "        if down_min_ratio_knee_foot <= knee_foot_ratio <= down_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 0\n",
+                "        elif knee_foot_ratio < down_min_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 1\n",
+                "        elif knee_foot_ratio > down_max_ratio_knee_foot:\n",
+                "            analyzed_results[\"knee_placement\"] = 2\n",
+                "    \n",
+                "    return analyzed_results\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 3. Make detection"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "VIDEO_PATH1 = \"../data/squat/squat_test_1.mov\"\n",
+                "VIDEO_PATH2 = \"../data/squat/squat_test_2.mov\"\n",
+                "VIDEO_PATH3 = \"../data/squat/squat_test_3.mp4\"\n",
+                "VIDEO_PATH4 = \"../data/squat/squat_right_2.mp4\""
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Load model for counter\n",
+                "with open(\"./model/squat_model.pkl\", \"rb\") as f:\n",
+                "    count_model = pickle.load(f)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "cap = cv2.VideoCapture(VIDEO_PATH3)\n",
+                "\n",
+                "# Counter vars\n",
+                "counter = 0\n",
+                "current_stage = \"\"\n",
+                "PREDICTION_PROB_THRESHOLD = 0.7\n",
+                "\n",
+                "# Error vars\n",
+                "VISIBILITY_THRESHOLD = 0.6\n",
+                "FOOT_SHOULDER_RATIO_THRESHOLDS = [1.2, 2.8]\n",
+                "KNEE_FOOT_RATIO_THRESHOLDS = {\n",
+                "    \"up\": [0.5, 1.0],\n",
+                "    \"middle\": [0.7, 1.0],\n",
+                "    \"down\": [0.7, 1.1],\n",
+                "}\n",
+                "\n",
+                "\n",
+                "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
+                "    while cap.isOpened():\n",
+                "        ret, image = cap.read()\n",
+                "\n",
+                "        if not ret:\n",
+                "            break\n",
+                "        \n",
+                "        # Reduce size of a frame\n",
+                "        image = rescale_frame(image, 100)\n",
+                "\n",
+                "        # Recolor image from BGR to RGB for mediapipe\n",
+                "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+                "        image.flags.writeable = False\n",
+                "\n",
+                "        results = pose.process(image)\n",
+                "        if not results.pose_landmarks:\n",
+                "            continue\n",
+                "\n",
+                "        # Recolor image from BGR to RGB for mediapipe\n",
+                "        image.flags.writeable = True\n",
+                "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+                "\n",
+                "        # Draw landmarks and connections\n",
+                "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
+                "\n",
+                "        # Make detection\n",
+                "        try:\n",
+                "            # * Model prediction for SQUAT counter\n",
+                "            # Extract keypoints from frame for the input\n",
+                "            row = extract_important_keypoints(results)\n",
+                "            X = pd.DataFrame([row], columns=headers[1:])\n",
+                "\n",
+                "            # Make prediction and its probability\n",
+                "            predicted_class = count_model.predict(X)[0]\n",
+                "            prediction_probabilities = count_model.predict_proba(X)[0]\n",
+                "            prediction_probability = round(prediction_probabilities[prediction_probabilities.argmax()], 2)\n",
+                "\n",
+                "            # Evaluate model prediction\n",
+                "            if predicted_class == \"down\" and prediction_probability >= PREDICTION_PROB_THRESHOLD:\n",
+                "                current_stage = \"down\"\n",
+                "            elif current_stage == \"down\" and predicted_class == \"up\" and prediction_probability >= PREDICTION_PROB_THRESHOLD: \n",
+                "                current_stage = \"up\"\n",
+                "                counter += 1\n",
+                "\n",
+                "            # Analyze squat pose\n",
+                "            analyzed_results = analyze_foot_knee_placement(results=results, stage=current_stage, foot_shoulder_ratio_thresholds=FOOT_SHOULDER_RATIO_THRESHOLDS, knee_foot_ratio_thresholds=KNEE_FOOT_RATIO_THRESHOLDS, visibility_threshold=VISIBILITY_THRESHOLD)\n",
+                "\n",
+                "            foot_placement_evaluation = analyzed_results[\"foot_placement\"]\n",
+                "            knee_placement_evaluation = analyzed_results[\"knee_placement\"]\n",
+                "            \n",
+                "            # * Evaluate FOOT PLACEMENT error\n",
+                "            if foot_placement_evaluation == -1:\n",
+                "                foot_placement = \"UNK\"\n",
+                "            elif foot_placement_evaluation == 0:\n",
+                "                foot_placement = \"Correct\"\n",
+                "            elif foot_placement_evaluation == 1:\n",
+                "                foot_placement = \"Too tight\"\n",
+                "            elif foot_placement_evaluation == 2:\n",
+                "                foot_placement = \"Too wide\"\n",
+                "            \n",
+                "            # * Evaluate KNEE PLACEMENT error\n",
+                "            if knee_placement_evaluation == -1:\n",
+                "                knee_placement = \"UNK\"\n",
+                "            elif knee_placement_evaluation == 0:\n",
+                "                knee_placement = \"Correct\"\n",
+                "            elif knee_placement_evaluation == 1:\n",
+                "                knee_placement = \"Too tight\"\n",
+                "            elif knee_placement_evaluation == 2:\n",
+                "                knee_placement = \"Too wide\"\n",
+                "            \n",
+                "            # Visualization\n",
+                "            # Status box\n",
+                "            cv2.rectangle(image, (0, 0), (500, 60), (245, 117, 16), -1)\n",
+                "\n",
+                "            # Display class\n",
+                "            cv2.putText(image, \"COUNT\", (10, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+                "            cv2.putText(image, f'{str(counter)}, {predicted_class.split(\" \")[0]}, {str(prediction_probability)}', (5, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
+                "\n",
+                "            # Display Foot and Shoulder width ratio\n",
+                "            cv2.putText(image, \"FOOT\", (200, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+                "            cv2.putText(image, foot_placement, (195, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
+                "\n",
+                "            # Display knee and Shoulder width ratio\n",
+                "            cv2.putText(image, \"KNEE\", (330, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
+                "            cv2.putText(image, knee_placement, (325, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
+                "\n",
+                "        except Exception as e:\n",
+                "            print(f\"Error: {e}\")\n",
+                "        \n",
+                "        cv2.imshow(\"CV2\", image)\n",
+                "        \n",
+                "        # Press Q to close cv2 window\n",
+                "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+                "            break\n",
+                "\n",
+                "    cap.release()\n",
+                "    cv2.destroyAllWindows()\n",
+                "\n",
+                "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
+                "    for i in range (1, 5):\n",
+                "        cv2.waitKey(1)\n",
+                "  "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": []
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3.8.13 (conda)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        },
+        "orig_nbformat": 4,
+        "vscode": {
+            "interpreter": {
+                "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
+            }
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
diff --git a/core/squat_model/README.md b/core/squat_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..755aa739bfc222321ab0de8049029eb7d53ffd14
--- /dev/null
+++ b/core/squat_model/README.md
@@ -0,0 +1,42 @@
+<h2 align="center">BICEP CURL MODEL TRAINING PROCESS</h2>
+
+### 1. Folder structure
+
+```
+bicep_model
+│   1.counter_model.ipynb - train models using Sklearn ML algo and evaluate the models results
+|   2.analyzed_bad_pose.ipynb - analyze feet and knee position in correct squat exercise
+│   3.detection.ipynb - detection on test videos
+|   train.csv - train dataset after converted from videos
+|   test.csv - test dataset after converted from videos
+|   evaluation.csv - models' evaluation results
+|   analyzed_pose.csv - analyzed feet and knee position dataset
+│
+└───model/ - folder contains best trained models and input scaler
+│   │
+```
+
+### 2. Important landmarks
+
+TThere are 3 popular errors of basic squat that will be targeted in this thesis:
+
+-   Feet placement: Feet placement is extremely important in squat. The 2 feet should place in a way that the width of 2 feet is around the width of 2 shoulders.
+-   Knee placement: Knee placement is not only important but could be dangerous if perform incorrectly with heavy load. During the “down” stage of the exercise, the knee should be open out to wider than the feet width.
+
+In my research and exploration, **_the important MediaPipe Pose landmarks_** for this exercise are: left shoulder, right shoulder, right hip, left hip, right knee, left knee, right ankle and left ankle.
+
+### 3. Stage detection
+
+In contrast with other exercises, there are 2 stages when performing squat, “up” and “down” stage. Because it is important for error detection to discriminate stage of the squat, a model is trained for this task. See this [notebook](./1.counter_model.ipynb) for a evaluation process for this model.
+
+### 3. Error detection method
+
+1. **Feet placement**: Can be detected by calculate ratio between the distance of 2 feet and the distance of 2 shoulders.
+
+    - Analyzed results:
+      <p align="center"><img src="../../images/squat_eval_4.png" alt="Logo" width="70%"></p>
+
+1. **Knee placement**: Can be detected by calculating ratio between the distance of 2 knee and 2 feet. Similar to the previous error, videos of contributors are analyzed to determine a correct threshold. Due to the dynamic movement of the knee during the exercise, the calculated ratio from the data will be separate into 3 stages: up, middle and down.
+
+    - Analyzed result:
+          <p align="center"><img src="../../images/squat_eval_5.png" alt="Logo" width="70%"></p>
diff --git a/core/squat_model/analyze_pose.csv b/core/squat_model/analyze_pose.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1b94871f3d97cd19faaadd96a841dbc9389ba58b
--- /dev/null
+++ b/core/squat_model/analyze_pose.csv
@@ -0,0 +1,852 @@
+video,stage,shoulder_width,feet_width,knee_width,ratio_feet_shoulder,ratio_knee_feet
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diff --git a/core/squat_model/evaluation.csv b/core/squat_model/evaluation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8dd0a16a1f7e52304fd005b879ecf5e3e11cd262
--- /dev/null
+++ b/core/squat_model/evaluation.csv
@@ -0,0 +1,15 @@
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+ [  3 420]]"
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+ [  6 417]]"
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+ [ 13 410]]"
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+ [ 20 403]]"
+DTC,0.2541200012644154,0.5041031652989449,0.5041031652989449,0.33790235553943304,"[[430   0]
+ [423   0]]"
+NB,0.2541200012644154,0.5041031652989449,0.5041031652989449,0.33790235553943304,"[[430   0]
+ [423   0]]"
+RF,0.2541200012644154,0.5041031652989449,0.5041031652989449,0.33790235553943304,"[[430   0]
+ [423   0]]"
diff --git a/core/squat_model/model/LR_model.pkl b/core/squat_model/model/LR_model.pkl
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index 0000000000000000000000000000000000000000..e20a1ab0d231e93bb87e1bcb6d5a339bdfe2eb2b
--- /dev/null
+++ b/core/squat_model/model/LR_model.pkl
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diff --git a/core/squat_model/model/SGDC_model.pkl b/core/squat_model/model/SGDC_model.pkl
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diff --git a/core/squat_model/model/sklearn_models.pkl b/core/squat_model/model/sklearn_models.pkl
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--- /dev/null
+++ b/core/squat_model/model/sklearn_models.pkl
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diff --git a/core/squat_model/test.csv b/core/squat_model/test.csv
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index 0000000000000000000000000000000000000000..f9a408f0ca74819d9882f00afd24d29ab9a48bb8
--- /dev/null
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diff --git a/core/squat_model/train.csv b/core/squat_model/train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8630855458bffe2eb0c96d88acbe71b90b565106
--- /dev/null
+++ b/core/squat_model/train.csv
@@ -0,0 +1,4161 @@
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new file mode 100644
index 0000000000000000000000000000000000000000..b52b445224f14ace49c79dd1005aeb3bfce9c3c4
--- /dev/null
+++ b/images/plank.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ccbeda2fb74178999b97793365dd11e0a2f1dafd29f09b0e6db1485796178ef
+size 5017637
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new file mode 100644
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+++ b/images/squat.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f9240810c609da8f43b1687f13ae476b168a74b13450e00e6c4f9747c905e76
+size 5457002
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diff --git a/images/web_3.png b/images/web_3.png
new file mode 100644
index 0000000000000000000000000000000000000000..ffa13b29ecfaf89f3a051e0059484f1f174b8906
--- /dev/null
+++ b/images/web_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97a0bbd712a490bb6f61e005e20a040359db6bad36d29bfe18186f49251da97d
+size 1885412
diff --git a/images/web_4.png b/images/web_4.png
new file mode 100644
index 0000000000000000000000000000000000000000..9f78a5ef1fb66f6f639ce58c59af4fb71f76025d
--- /dev/null
+++ b/images/web_4.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3356739498f3d06882c77b9b851dcd31d4c665cdff76a147dbca24ecd05a10b5
+size 1818242
diff --git a/package.json b/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..881f43f24269d412d4e996b969453d2efb1e896b
--- /dev/null
+++ b/package.json
@@ -0,0 +1,24 @@
+{
+    "name": "exercise-correction",
+    "version": "1.0.0",
+    "description": "",
+    "main": "index.js",
+    "scripts": {
+        "install:website": "cd ./web && npm run install:all",
+        "install:client": "cd ./web && npm run install:client",
+        "start:website": "cd ./web && npm run start:website",
+        "remove-static-from-web": "cd ./web/server && python manage.py runscript static_remover",
+        "build:docker": "docker build -t ec .",
+        "run:docker": "docker run -e VITE_BASE_URL=http://127.0.0.1 -p 80:8000 ec"
+    },
+    "repository": {
+        "type": "git",
+        "url": "git+https://github.com/NgoQuocBao1010/Exercise-Correction.git"
+    },
+    "author": "",
+    "license": "ISC",
+    "bugs": {
+        "url": "https://github.com/NgoQuocBao1010/Exercise-Correction/issues"
+    },
+    "homepage": "https://github.com/NgoQuocBao1010/Exercise-Correction#readme"
+}
diff --git a/requirements-mac.txt b/requirements-mac.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5743807bb962e3f7f7d2413df7c19268ddd34c5a
--- /dev/null
+++ b/requirements-mac.txt
@@ -0,0 +1,15 @@
+opencv-python==4.6.0.66
+mediapipe-silicon==0.8.10.1
+keras==2.9.0
+keras-tuner==1.1.3
+tensorflow-macos==2.9.2
+sklearn==1.1.2
+numpy==1.23.3
+panda==1.4.3
+matplotlib==3.6.0
+seaborn==0.12.0
+yellowbrick==1.5
+Django==4.1.2
+djangorestframework==3.14.0
+django-cors-headers==3.13.0
+django-extensions==3.2.1
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3bc3f99d88e0a4a0b93cd0d93a6989586ee66ae5
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+opencv-python==4.6.0.66
+mediapipe
+keras==2.9.0
+keras-tuner==1.1.3
+scikit-learn==1.1.2
+numpy==1.23.3
+panda==1.4.3
+matplotlib==3.6.0
+seaborn==0.12.0
+yellowbrick==1.5
+Django==4.1.2
+djangorestframework==3.14.0
+django-cors-headers==3.13.0
+django-extensions==3.2.1
+protobuf==3.20.*
diff --git a/web/README.md b/web/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d16d271de7bafee30c484ec300463d84f2e715a2
--- /dev/null
+++ b/web/README.md
@@ -0,0 +1,40 @@
+<h2 align="center">Website for Models Demonstration</h2>
+
+Dependencies install in this section do not contain other machine learning packages, they just contain enough packages to run the website.
+
+#### Setting Up Environment
+
+```
+    Python 3.8.13
+    Node 17.8.0
+    NPM 8.5.5
+    OS: Linux or MacOS
+```
+
+Installation
+
+1. Clone the repo and change directory to that folder _(skip this part if it is already cloned)_
+
+    ```sh
+    git clone https://github.com/NgoQuocBao1010/Exercise-Correction.git
+    ```
+
+1. Change directory to _./web_ folder
+
+    ```bash
+    cd web
+    ```
+
+1. Install all dependencies for client and server _(skip this part if they are already installed)_
+
+    ```bash
+    npm run install:all
+    ```
+
+1. Start website - Run on http://127.0.0.1:8000/
+
+    ```bash
+    npm run start:website
+    ```
+
+1. Look through [here](./package.json) for other commands to run dev server.
diff --git a/web/client/README.md b/web/client/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c12e12e6f88b7279764f6df0c310b73836feae03
--- /dev/null
+++ b/web/client/README.md
@@ -0,0 +1,35 @@
+# exercise-correction
+
+This template should help get you started developing with Vue 3 in Vite.
+
+## Recommended IDE Setup
+
+[VSCode](https://code.visualstudio.com/) + [Volar](https://marketplace.visualstudio.com/items?itemName=Vue.volar) (and disable Vetur) + [TypeScript Vue Plugin (Volar)](https://marketplace.visualstudio.com/items?itemName=Vue.vscode-typescript-vue-plugin).
+
+## Customize configuration
+
+See [Vite Configuration Reference](https://vitejs.dev/config/).
+
+## Project Setup
+
+```sh
+npm install
+```
+
+### Compile and Hot-Reload for Development
+
+```sh
+npm run dev
+```
+
+### Compile and Minify for Production
+
+```sh
+npm run build
+```
+
+### Run Unit Tests with [Vitest](https://vitest.dev/)
+
+```sh
+npm run test:unit
+```
diff --git a/web/client/index.html b/web/client/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..2b079b73284a921f5ab2e82e2a863197946382b4
--- /dev/null
+++ b/web/client/index.html
@@ -0,0 +1,20 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <link rel="icon" href="/favicon.ico" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <link
+            rel="stylesheet"
+            href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.2.0/css/all.min.css"
+            integrity="sha512-xh6O/CkQoPOWDdYTDqeRdPCVd1SpvCA9XXcUnZS2FmJNp1coAFzvtCN9BmamE+4aHK8yyUHUSCcJHgXloTyT2A=="
+            crossorigin="anonymous"
+            referrerpolicy="no-referrer"
+        />
+        <title>Exercise Correction</title>
+    </head>
+    <body>
+        <div id="app"></div>
+        <script type="module" src="/src/main.js"></script>
+    </body>
+</html>
diff --git a/web/client/package.json b/web/client/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb45dde113790289babae4e7cf1841781ec84354
--- /dev/null
+++ b/web/client/package.json
@@ -0,0 +1,29 @@
+{
+    "name": "exercise-correction",
+    "version": "0.0.0",
+    "scripts": {
+        "dev": "vite",
+        "build": "vite build",
+        "build:deploy": "npm run set-base-url && npm run build && npm run move-build-to-server",
+        "preview": "vite preview --port 4173",
+        "test:unit": "vitest --environment jsdom",
+        "move:assets": "rsync -a --delete ./dist/static/* ../server/static/",
+        "move:index": "cp -fr ./dist/index.html ../server/templates/",
+        "move-build-to-server": "npm run move:assets && npm run move:index",
+        "set-base-url": "rm -f .env.local && echo \"VITE_BASE_URL=http://127.0.0.1\" >| .env.local"
+    },
+    "dependencies": {
+        "axios": "^1.1.2",
+        "pinia": "^2.0.21",
+        "sass": "^1.55.0",
+        "vue": "^3.2.38",
+        "vue-router": "^4.1.5"
+    },
+    "devDependencies": {
+        "@vitejs/plugin-vue": "^3.0.3",
+        "@vue/test-utils": "^2.0.2",
+        "jsdom": "^20.0.0",
+        "vite": "^3.0.9",
+        "vitest": "^0.23.0"
+    }
+}
diff --git a/web/client/public/favicon.ico b/web/client/public/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..df36fcfb72584e00488330b560ebcf34a41c64c2
Binary files /dev/null and b/web/client/public/favicon.ico differ
diff --git a/web/client/src/App.vue b/web/client/src/App.vue
new file mode 100644
index 0000000000000000000000000000000000000000..00f530ea05cb02d78f2e6a8680462e3c5bed0b4b
--- /dev/null
+++ b/web/client/src/App.vue
@@ -0,0 +1,89 @@
+<script setup></script>
+
+<template>
+    <div class="site-container">
+        <!-- Navbar -->
+        <nav>
+            <RouterLink :to="{ name: 'Home' }" class="nav__header">
+                Exercise Correction
+            </RouterLink>
+        </nav>
+
+        <!-- Main Content -->
+        <main>
+            <!-- Page View -->
+            <RouterView />
+        </main>
+
+        <!-- Footer -->
+        <footer>
+            <a
+                href="https://twitter.com/ngohquocbao1010"
+                target="_blank"
+                class="twitter"
+            >
+                @NgoQuocBao
+            </a>
+
+            <a
+                href="https://github.com/NgoQuocBao1010/Exercise-Correction"
+                target="_blank"
+                class="github"
+            >
+                <i class="fa-brands fa-github"></i>
+            </a>
+        </footer>
+    </div>
+</template>
+
+<style lang="scss">
+body {
+    .site-container {
+        min-height: 100vh;
+        display: flex;
+        flex-direction: column;
+    }
+
+    nav {
+        margin: 3rem 0;
+        text-align: center;
+
+        .nav__header {
+            font-size: 2rem;
+            font-family: var(--font-family);
+            font-weight: 700;
+            text-transform: uppercase;
+            letter-spacing: 1px;
+            color: var(--primary-color);
+        }
+    }
+
+    main {
+        margin-inline: auto;
+        width: 90vw;
+    }
+
+    footer {
+        margin-top: auto;
+        margin-inline: auto;
+        width: 90vw;
+        border-top: 1px solid var(--primary-color);
+        padding: 0.3rem 0;
+        text-align: center;
+
+        a {
+            font-family: var(--font-family);
+            color: var(--primary-color);
+        }
+
+        .twitter {
+            font-weight: 700;
+        }
+
+        .github {
+            font-size: 1.2rem;
+            margin-left: 1rem;
+        }
+    }
+}
+</style>
diff --git a/web/client/src/assets/css/default.css b/web/client/src/assets/css/default.css
new file mode 100644
index 0000000000000000000000000000000000000000..95e44581ba363b6e1fa6165e3ad41500b4d3cb29
--- /dev/null
+++ b/web/client/src/assets/css/default.css
@@ -0,0 +1,22 @@
+@import url("https://fonts.googleapis.com/css2?family=Poppins:wght@400;600;700;900&family=Roboto:wght@400;700;900&display=swap");
+
+:root {
+    --primary-color: #41b883;
+    --primary-color-rbg: 65, 184, 131;
+    --primary-color-text: #1e2d50;
+    --secondary-color: #34495e;
+    --font-family: "Poppins", sans-serif;
+    --vn-font-family: "Roboto", sans-serif;
+}
+
+* {
+    font-family: var(--font-family);
+}
+
+html {
+    scroll-behavior: smooth;
+}
+
+.invisible {
+    visibility: hidden;
+}
diff --git a/web/client/src/assets/css/reset.css b/web/client/src/assets/css/reset.css
new file mode 100644
index 0000000000000000000000000000000000000000..40bd69a775732600227128cee49eb64d18261c95
--- /dev/null
+++ b/web/client/src/assets/css/reset.css
@@ -0,0 +1,127 @@
+html,
+body,
+div,
+span,
+applet,
+object,
+iframe,
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+p,
+blockquote,
+pre,
+a,
+abbr,
+acronym,
+address,
+big,
+cite,
+code,
+del,
+dfn,
+em,
+img,
+ins,
+kbd,
+q,
+s,
+samp,
+small,
+strike,
+strong,
+sub,
+sup,
+tt,
+var,
+b,
+u,
+i,
+center,
+dl,
+dt,
+dd,
+ol,
+ul,
+li,
+fieldset,
+form,
+label,
+legend,
+table,
+caption,
+tbody,
+tfoot,
+thead,
+tr,
+th,
+td,
+article,
+aside,
+canvas,
+details,
+embed,
+figure,
+figcaption,
+footer,
+header,
+hgroup,
+menu,
+nav,
+output,
+ruby,
+section,
+summary,
+time,
+mark,
+audio,
+video {
+    margin: 0;
+    padding: 0;
+    border: 0;
+    font-size: 100%;
+    font: inherit;
+    vertical-align: baseline;
+}
+/* HTML5 display-role reset for older browsers */
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+menu,
+nav,
+section {
+    display: block;
+}
+body {
+    line-height: 1;
+}
+ol,
+ul {
+    list-style: none;
+}
+blockquote,
+q {
+    quotes: none;
+}
+blockquote:before,
+blockquote:after,
+q:before,
+q:after {
+    content: "";
+    content: none;
+}
+table {
+    border-collapse: collapse;
+    border-spacing: 0;
+}
+a {
+    text-decoration: none;
+}
diff --git a/web/client/src/assets/logo.svg b/web/client/src/assets/logo.svg
new file mode 100644
index 0000000000000000000000000000000000000000..bc826fed80ad0c846e5ca25978776f555f4a2370
--- /dev/null
+++ b/web/client/src/assets/logo.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 261.76 226.69"  xmlns:v="https://vecta.io/nano"><path d="M161.096.001l-30.225 52.351L100.647.001H-.005l130.877 226.688L261.749.001z" fill="#41b883"/><path d="M161.096.001l-30.225 52.351L100.647.001H52.346l78.526 136.01L209.398.001z" fill="#34495e"/></svg>
\ No newline at end of file
diff --git a/web/client/src/components/Dropzone.vue b/web/client/src/components/Dropzone.vue
new file mode 100644
index 0000000000000000000000000000000000000000..2ddec502e76806e279d511a9d5a5d3a034c92981
--- /dev/null
+++ b/web/client/src/components/Dropzone.vue
@@ -0,0 +1,133 @@
+<script setup>
+import { ref, watch, computed } from "vue";
+
+// TODO: handle only video file
+const dropzoneInput = ref(null);
+const uploadedVideoFile = ref(null);
+const isUploaded = computed(() => (uploadedVideoFile.value ? true : false));
+const isDragOver = ref(false);
+
+// Emit events
+const emit = defineEmits(["fileUploaded"]);
+watch(
+    () => uploadedVideoFile.value,
+    (newData, oldData) => {
+        if (newData) {
+            emit("fileUploaded", newData);
+        }
+    }
+);
+
+// * Handle Drag Events
+const removeDragEventDefault = (event) => {
+    event.preventDefault();
+    event.stopPropagation();
+};
+const handleDragOver = (event) => {
+    removeDragEventDefault(event);
+    isDragOver.value = true;
+};
+const handleDragLeave = (event) => {
+    removeDragEventDefault(event);
+    isDragOver.value = false;
+};
+const handleDrop = (event) => {
+    removeDragEventDefault(event);
+    isDragOver.value = false;
+
+    let videoFile = event.dataTransfer.files[0];
+
+    let dataTransfer = new DataTransfer();
+    dataTransfer.items.add(videoFile);
+    let filesToBeAdded = dataTransfer.files;
+
+    dropzoneInput.value.files = filesToBeAdded;
+    uploadedVideoFile.value = videoFile;
+};
+
+// Handle File Input
+const openFileInput = () => {
+    dropzoneInput.value.click();
+};
+const handleClickUpload = (event) => {
+    const target = event.target;
+    if (target && target.files) {
+        uploadedVideoFile.value = target.files[0];
+    }
+};
+
+// Other utils func
+const byteToMB = (bytes) => {
+    return Math.round(bytes / 1000000);
+};
+</script>
+
+<template>
+    <div
+        class="dropzone"
+        :class="{ 'dropzone-dragging': isDragOver }"
+        id="dropzone"
+        @drag="removeDragEventDefault"
+        @dragstart="removeDragEventDefault"
+        @dragend="removeDragEventDefault"
+        @dragover="handleDragOver"
+        @dragenter="handleDragLeave"
+        @dragleave="removeDragEventDefault"
+        @drop="handleDrop"
+        @click.self="openFileInput"
+    >
+        <!-- Initial Stage -->
+        <template v-if="!isUploaded">
+            <i class="fa-solid fa-cloud-arrow-up dropzone-icon"></i>
+            Drop files or Click here to select files to upload.
+        </template>
+
+        <!-- Uploaded State -->
+        <template v-else>
+            <i class="fa-regular fa-file-video dropzone-icon"></i>
+            {{ uploadedVideoFile.name }}
+            ({{ byteToMB(uploadedVideoFile.size) }} MB)
+        </template>
+
+        <input
+            type="file"
+            name="files"
+            class="dropzone-input"
+            ref="dropzoneInput"
+            @change="handleClickUpload"
+        />
+    </div>
+</template>
+
+<style lang="scss" scoped>
+.dropzone {
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    border: 0.2rem dashed var(--primary-color);
+    padding: 2rem;
+    border-radius: 0.25rem;
+    background-color: #fff;
+    font-size: 1.25rem;
+    text-align: center;
+    transition: 0.25s background-color ease-in-out;
+    cursor: pointer;
+
+    &-dragging,
+    &:hover {
+        background-color: lighten(#41b883, 38%);
+    }
+
+    &-icon {
+        display: block;
+        font-size: 3rem;
+        margin: 0 auto 1.5rem;
+        color: var(--primary-color);
+    }
+
+    &-input {
+        display: none;
+    }
+}
+</style>
diff --git a/web/client/src/components/DropzoneLoading.vue b/web/client/src/components/DropzoneLoading.vue
new file mode 100644
index 0000000000000000000000000000000000000000..504b2ce368bdb65a2a14307dfd5a736e1f35901b
--- /dev/null
+++ b/web/client/src/components/DropzoneLoading.vue
@@ -0,0 +1,76 @@
+<script setup></script>
+
+<template>
+    <div class="dropzone">
+        <div class="loading-animation">
+            <div class="dot"></div>
+            <div class="dot"></div>
+            <div class="dot"></div>
+        </div>
+
+        Processing ...
+    </div>
+</template>
+
+<style lang="scss" scoped>
+.dropzone {
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    border: 0.2rem dashed var(--primary-color);
+    padding: 2rem;
+    border-radius: 0.25rem;
+    background-color: #fff;
+    font-size: 1.25rem;
+    transition: 0.25s background-color ease-in-out;
+    cursor: pointer;
+
+    &-icon {
+        display: block;
+        font-size: 3rem;
+        margin: 0 auto 1.5rem;
+        color: var(--primary-color);
+    }
+
+    &-input {
+        display: none;
+    }
+}
+
+.loading-animation {
+    display: flex;
+    gap: 1rem;
+    margin-bottom: 2rem;
+
+    .dot {
+        display: inline-block;
+        width: 20px;
+        aspect-ratio: 1 / 1;
+        border-radius: 100%;
+        background-color: var(--primary-color);
+    }
+}
+
+.loading-animation .dot:nth-last-child(1) {
+    animation: loading 0.6s 0.1s linear infinite;
+}
+.loading-animation .dot:nth-last-child(2) {
+    animation: loading 0.6s 0.2s linear infinite;
+}
+.loading-animation .dot:nth-last-child(3) {
+    animation: loading 0.6s 0.3s linear infinite;
+}
+
+@keyframes loading {
+    0% {
+        transform: translate(0, 0);
+    }
+    50% {
+        transform: translate(0, 15px);
+    }
+    100% {
+        transform: translate(0, 0);
+    }
+}
+</style>
diff --git a/web/client/src/components/Result.vue b/web/client/src/components/Result.vue
new file mode 100644
index 0000000000000000000000000000000000000000..474749f9c6b5c7728ea22dc0c8c7b12964c9ec47
--- /dev/null
+++ b/web/client/src/components/Result.vue
@@ -0,0 +1,236 @@
+<script setup>
+import { ref, computed } from "vue";
+
+import Video from "./Video.vue";
+
+const { data } = defineProps(["data"]);
+const summaryData = computed(() => {
+    let results = {
+        total: 0,
+        totalInString: "",
+        details: {},
+    };
+
+    let totalErrors = data.details.length;
+    results.total = totalErrors;
+    if (totalErrors == 0 || totalErrors == 1)
+        results.totalInString = `${totalErrors} error`;
+    else results.totalInString = `${totalErrors} errors`;
+
+    data.details.forEach((error) => {
+        let stage = error.stage;
+        results.details[stage] = results.details[stage]
+            ? results.details[stage] + 1
+            : 1;
+    });
+
+    return results;
+});
+const selectedDisplay = ref("summary");
+const videoStart = ref(0);
+
+const jumpToVideoLocation = (second) => {
+    selectedDisplay.value = "video";
+    videoStart.value = second;
+};
+</script>
+
+<template>
+    <section class="result-section">
+        <!-- Navigators -->
+        <ul class="tab-links">
+            <li
+                :class="{ active: selectedDisplay == 'summary' }"
+                @click="() => (selectedDisplay = 'summary')"
+            >
+                Summary
+            </li>
+            <li
+                :class="{ active: selectedDisplay == 'detail' }"
+                @click="() => (selectedDisplay = 'detail')"
+            >
+                Detail
+            </li>
+            <li
+                :class="{ active: selectedDisplay == 'video' }"
+                @click="() => (selectedDisplay = 'video')"
+            >
+                Full Video
+            </li>
+        </ul>
+
+        <!-- Contents -->
+        <div class="tab-container">
+            <!-- Summary content -->
+            <template v-if="selectedDisplay == 'summary'">
+                <!-- Display Counter or other information -->
+                <p class="main" v-if="data.counter">
+                    <span class="info-color" v-if="data.type != 'bicep_curl'">
+                        Counter: {{ data.counter }}
+                    </span>
+
+                    <span class="info-color" v-else>
+                        Left arm counter: {{ data.counter.left_counter }} -
+                        Right arm counter: {{ data.counter.right_counter }}
+                    </span>
+                </p>
+
+                <!-- Display error -->
+                <p class="main">
+                    There are
+                    <span class="error-color">
+                        {{ summaryData.totalInString }}
+                    </span>
+                    found.
+
+                    <!-- Icon -->
+                    <i
+                        class="fa-solid fa-circle-exclamation error-color"
+                        v-if="summaryData.total > 0"
+                    ></i>
+                    <i class="fa-solid fa-circle-check" v-else></i>
+                </p>
+
+                <ul class="errors" v-if="summaryData.total > 0">
+                    <li v-for="(total, error) in summaryData.details">
+                        <i class="fa-solid fa-caret-right"></i>
+
+                        {{ error }}: {{ total }}
+                    </li>
+                </ul>
+            </template>
+
+            <!-- Detail Content -->
+            <KeepAlive>
+                <template v-if="selectedDisplay == 'detail'">
+                    <div
+                        class="box-error"
+                        v-for="(error, index) in data.details"
+                    >
+                        <p>
+                            {{ index + 1 }}. {{ error.stage }} at
+                            <span
+                                class="error-time"
+                                @click="jumpToVideoLocation(error.timestamp)"
+                            >
+                                {{ error.timestamp }} second
+                            </span>
+                        </p>
+                        <img :src="`${error.frame}`" />
+                        <hr />
+                    </div>
+                </template>
+            </KeepAlive>
+
+            <!-- Full Video content -->
+            <KeepAlive>
+                <template v-if="selectedDisplay == 'video'">
+                    <div class="video-container">
+                        <Video
+                            :video-name="data.file_name"
+                            :start-at="videoStart"
+                        ></Video>
+                    </div>
+                </template>
+            </KeepAlive>
+        </div>
+    </section>
+</template>
+
+<style lang="scss" scoped>
+.result-section {
+    margin-top: 2rem;
+    margin-bottom: 5rem;
+
+    .tab-links {
+        display: flex;
+
+        li {
+            width: 6em;
+            padding: 0.75rem 1rem;
+            padding-right: 1.2rem;
+            background-color: rgb(180, 179, 179);
+            border-top-left-radius: 1rem;
+            border-top-right-radius: 1rem;
+            font-size: 1rem;
+            text-transform: uppercase;
+            cursor: pointer;
+            transition: all 0.2s ease;
+
+            &.active {
+                background-color: var(--primary-color);
+            }
+
+            &:hover {
+                background-color: rgba($color: #41b883, $alpha: 0.4);
+            }
+        }
+    }
+
+    .tab-container {
+        padding: 1rem 2rem;
+        border: 3px solid var(--primary-color);
+
+        p.main {
+            font-size: 1.5rem;
+            margin: 1rem 0;
+
+            i {
+                font-size: 1.5rem;
+            }
+        }
+
+        ul.errors {
+            li {
+                margin: 0.75rem 0;
+                font-size: 1.2rem;
+                text-transform: capitalize;
+
+                i {
+                    margin-right: 1rem;
+                }
+            }
+        }
+
+        .box-error {
+            margin-bottom: 2rem;
+
+            p {
+                font-size: 1.2rem;
+                text-transform: capitalize;
+                margin-bottom: 0.5rem;
+            }
+
+            img {
+                width: 500px;
+            }
+
+            span.error-time {
+                color: rgb(85, 149, 171);
+                cursor: pointer;
+            }
+
+            hr {
+                background-color: var(--primary-color);
+                color: var(--primary-color);
+            }
+        }
+
+        .video-container {
+            width: 80%;
+            margin-inline: auto;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+        }
+    }
+
+    .error-color {
+        color: red;
+    }
+
+    .info-color {
+        color: rgb(55, 194, 55);
+    }
+}
+</style>
diff --git a/web/client/src/components/Video.vue b/web/client/src/components/Video.vue
new file mode 100644
index 0000000000000000000000000000000000000000..5758354147e13812f6dfca147a9de5c9ff61e38a
--- /dev/null
+++ b/web/client/src/components/Video.vue
@@ -0,0 +1,54 @@
+<script setup>
+import { ref, computed, onMounted } from "vue";
+
+const apiUrl = import.meta.env.VITE_BASE_URL;
+
+const { videoName, startAt } = defineProps({
+    videoName: String,
+    startAt: {
+        required: false,
+        type: Number,
+    },
+});
+
+const url = computed(
+    () => `${apiUrl}/api/video/stream?video_name=${videoName}`
+    // () => `http://baobao.com`
+);
+
+const video = ref(null);
+const videoContainer = ref(null);
+onMounted(() => {
+    if (startAt) video.value.currentTime = startAt;
+});
+
+const handleVideoLoad = () => {
+    videoContainer.value.scrollIntoView({
+        behavior: "smooth",
+        block: "center",
+    });
+};
+</script>
+
+<template>
+    <!-- Video Player -->
+    <p>{{ url }}</p>
+    <div class="player" ref="videoContainer">
+        <video
+            controls
+            ref="video"
+            @loadeddata="handleVideoLoad"
+            autoplay
+            loop
+            muted
+        >
+            <source :src="`${url}`" type="video/mp4" />
+        </video>
+    </div>
+</template>
+
+<style lang="scss" scoped>
+video {
+    width: 100%;
+}
+</style>
diff --git a/web/client/src/components/Webcam.vue b/web/client/src/components/Webcam.vue
new file mode 100644
index 0000000000000000000000000000000000000000..c38ea69ebc519fa1fc9183a5e2225a977b5013dd
--- /dev/null
+++ b/web/client/src/components/Webcam.vue
@@ -0,0 +1,73 @@
+<script setup>
+import { ref, watch } from "vue";
+
+const camera = ref(null);
+const picture = ref(null);
+
+const getVideo = () => {
+    navigator.mediaDevices
+        .getUserMedia({ video: true })
+        .then((stream) => {
+            let video = camera.value;
+            video.srcObject = stream;
+            video.play();
+        })
+        .catch((err) => console.log(err));
+};
+
+const takeSnapshot = () => {
+    const width = 500;
+    const height = 500;
+
+    let video = camera.value;
+
+    picture.value.width = width;
+    picture.value.height = height;
+
+    let ctx = picture.value.getContext("2d");
+    ctx.drawImage(video, 0, 0, width, height);
+};
+
+watch(camera, () => {
+    getVideo();
+});
+</script>
+
+<template>
+    <div class="camera">
+        <div class="camera__wrapper">
+            <video ref="camera"></video>
+        </div>
+
+        <button @click="takeSnapshot">SNAP!</button>
+    </div>
+
+    <div class="result">
+        <canvas ref="picture"></canvas>
+    </div>
+</template>
+
+<style lang="scss" scoped>
+.camera {
+    background-color: rgba(0, 255, 255, 0.5);
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    gap: 2rem;
+
+    &__wrapper {
+        width: 60%;
+        overflow: hidden;
+
+        video {
+            width: 100%;
+            height: auto;
+        }
+    }
+}
+
+.result {
+    margin-top: 2rem;
+}
+</style>
diff --git a/web/client/src/main.js b/web/client/src/main.js
new file mode 100644
index 0000000000000000000000000000000000000000..4598a24a3c3084ea7dad428dc07e470d94bda9f5
--- /dev/null
+++ b/web/client/src/main.js
@@ -0,0 +1,17 @@
+// Default CSS
+import "./assets/css/default.css";
+import "./assets/css/reset.css";
+
+// JS
+import { createPinia } from "pinia";
+import { createApp } from "vue";
+
+import App from "./App.vue";
+import router from "./router";
+
+const app = createApp(App);
+
+app.use(createPinia());
+app.use(router);
+
+app.mount("#app");
diff --git a/web/client/src/router/index.js b/web/client/src/router/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..2e49bc361146736ecf11ad31010b59d5135164fd
--- /dev/null
+++ b/web/client/src/router/index.js
@@ -0,0 +1,23 @@
+import { createRouter, createWebHistory } from "vue-router";
+
+const router = createRouter({
+    history: createWebHistory(import.meta.env.BASE_URL),
+    routes: [
+        {
+            path: "/",
+            name: "Home",
+            component: () => import("../views/Home.vue"),
+        },
+        {
+            path: "/video",
+            name: "VideoStreaming",
+            component: () => import("../views/VideoStreaming.vue"),
+        },
+        {
+            path: "/:pathMatch(.*)*",
+            redirect: { name: "Home" },
+        },
+    ],
+});
+
+export default router;
diff --git a/web/client/src/views/Home.vue b/web/client/src/views/Home.vue
new file mode 100644
index 0000000000000000000000000000000000000000..74bcd3c2916fd2779129c8255a1e254d8205d516
--- /dev/null
+++ b/web/client/src/views/Home.vue
@@ -0,0 +1,61 @@
+<script setup></script>
+
+<template>
+    <div class="container">
+        <RouterLink to="#" class="box">
+            <i class="fa-solid fa-video"></i>
+            <p class="box__description">Real Time</p>
+        </RouterLink>
+
+        <RouterLink :to="{ name: 'VideoStreaming' }" class="box">
+            <i class="fa-solid fa-upload"></i>
+            <p class="box__description">Video Upload</p>
+        </RouterLink>
+    </div>
+</template>
+
+<style lang="scss" scoped>
+.container {
+    margin-top: 10rem;
+    width: 100%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4rem;
+
+    .box {
+        width: 20%;
+        max-width: 200px;
+        aspect-ratio: 1 / 1;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        flex-direction: column;
+        gap: 1.2rem;
+        cursor: pointer;
+        border: 4px solid var(--primary-color);
+        border-radius: 10%;
+        box-shadow: rgba(255, 255, 255, 0.1) 0px 1px 1px 0px inset,
+            rgba(50, 50, 93, 0.25) 0px 50px 100px -20px,
+            rgba(0, 0, 0, 0.3) 0px 30px 60px -30px;
+        transition: all 0.2s ease-in;
+
+        i {
+            font-size: 3rem;
+            color: var(--primary-color);
+        }
+
+        &__description {
+            font-size: 1.2rem;
+            font-weight: 500;
+            text-transform: uppercase;
+            color: var(--secondary-color);
+            transform: translateY(10px);
+        }
+
+        &:hover {
+            transform: scale(1.05);
+        }
+    }
+}
+</style>
diff --git a/web/client/src/views/VideoStreaming.vue b/web/client/src/views/VideoStreaming.vue
new file mode 100644
index 0000000000000000000000000000000000000000..3c96c180984ee5f84c27015297325b7707db1e19
--- /dev/null
+++ b/web/client/src/views/VideoStreaming.vue
@@ -0,0 +1,150 @@
+<script setup>
+import { ref } from "vue";
+import axios from "axios";
+
+import Dropzone from "../components/Dropzone.vue";
+import DropzoneLoading from "../components/DropzoneLoading.vue";
+import Result from "../components/Result.vue";
+
+const apiUrl = import.meta.env.VITE_BASE_URL || "http://127.0.0.1:8000";
+
+const EXERCISES = ["squat", "plank", "bicep_curl", "lunge"];
+
+const submitData = ref({
+    videoFile: null,
+    exerciseType: null,
+});
+const processedData = ref(null);
+const isProcessing = ref(false);
+
+const uploadToServer = async () => {
+    if (!submitData.value.videoFile) {
+        alert("No video selected");
+        return;
+    }
+
+    if (!submitData.value.exerciseType) {
+        alert("No exercise type selected");
+        return;
+    }
+
+    processedData.value = null;
+    try {
+        isProcessing.value = true;
+        const { data } = await axios.post(
+            `${apiUrl}/api/video/upload?type=${submitData.value.exerciseType}`,
+            { file: submitData.value.videoFile },
+            {
+                headers: {
+                    "Content-Type": "multipart/form-data",
+                },
+            }
+        );
+        processedData.value = data;
+    } catch (e) {
+        console.error("Error: ", e);
+    } finally {
+        isProcessing.value = false;
+    }
+};
+</script>
+
+<template>
+    <!-- Input section -->
+    <section class="input-section">
+        <Dropzone
+            v-show="!isProcessing"
+            @file-uploaded="(file) => (submitData.videoFile = file)"
+        />
+        <DropzoneLoading v-show="isProcessing" />
+
+        <div class="right-container">
+            <!-- exercises selection -->
+            <div class="exercises-container">
+                <p
+                    class="exercise"
+                    v-for="exercise in EXERCISES"
+                    :class="{ active: submitData.exerciseType == exercise }"
+                    @click="submitData.exerciseType = exercise"
+                >
+                    {{ exercise }}
+                </p>
+            </div>
+
+            <button class="process-btn" @click="uploadToServer">
+                <span>Process!</span>
+            </button>
+        </div>
+    </section>
+
+    <!-- Results section -->
+    <Result v-if="processedData" :data="processedData" />
+</template>
+
+<style lang="scss" scoped>
+.input-section {
+    display: flex;
+    gap: 1rem;
+
+    * {
+        flex: 1;
+    }
+
+    .right-container {
+        display: flex;
+        flex-direction: column;
+        width: 100%;
+
+        .exercises-container {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 1rem;
+            margin-bottom: 1rem;
+
+            .exercise {
+                display: flex;
+                justify-content: center;
+                align-items: center;
+                padding: 1rem 0;
+                flex: 45%;
+                color: var(--secondary-color);
+                text-transform: uppercase;
+                border: 3px solid var(--primary-color);
+                border-radius: 0.3rem;
+                cursor: pointer;
+                transition: all 0.25s ease;
+
+                &:hover {
+                    box-shadow: 0 6px 18px 0 rgba(#000, 0.1);
+                    transform: translateY(-6px);
+                }
+
+                &.active {
+                    background-color: var(--primary-color);
+                    font-weight: 700;
+                }
+            }
+        }
+
+        .process-btn {
+            border: none;
+            background-color: var(--primary-color);
+            padding: 1.25rem 0;
+
+            color: whitesmoke;
+            font-size: 1.25rem;
+            font-weight: 700;
+            cursor: pointer;
+            border-radius: 8px;
+            transition: all 0.25s ease;
+
+            &:hover {
+                box-shadow: 0 6px 18px 0 rgba(#000, 0.1);
+                color: var(--primary-color);
+                border-color: transparent;
+                background-color: transparent;
+            }
+        }
+    }
+}
+</style>
diff --git a/web/client/vite.config.js b/web/client/vite.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..f705a177ed49adb977717186d3af5a2db0fc6f7b
--- /dev/null
+++ b/web/client/vite.config.js
@@ -0,0 +1,29 @@
+import { fileURLToPath, URL } from "node:url";
+
+import vue from "@vitejs/plugin-vue";
+import { defineConfig } from "vite";
+
+// https://vitejs.dev/config/
+export default defineConfig({
+    plugins: [vue()],
+    resolve: {
+        alias: {
+            "@": fileURLToPath(new URL("./src", import.meta.url)),
+        },
+    },
+    build: {
+        rollupOptions: {
+            output: {
+                assetFileNames: (assetInfo) => {
+                    let extType = assetInfo.name.split(".").at(1);
+                    if (/png|jpe?g|svg|gif|tiff|bmp|ico/i.test(extType)) {
+                        extType = "img";
+                    }
+                    return `static/${extType}/[name]-[hash][extname]`;
+                },
+                chunkFileNames: "static/assets/js/[name]-[hash].js",
+                entryFileNames: "static/assets/js/[name]-[hash].js",
+            },
+        },
+    },
+});
diff --git a/web/package.json b/web/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa65337001cb2bd903da83273398e22138c0ea4e
--- /dev/null
+++ b/web/package.json
@@ -0,0 +1,18 @@
+{
+    "name": "web",
+    "version": "1.0.0",
+    "description": "<h2 align=\"center\">Website for Models Demonstration</h2>",
+    "main": "index.js",
+    "scripts": {
+        "install:client": "cd ./client && npm i",
+        "dev:client": "cd ./client && npm run dev",
+        "build-deploy:client": "cd ./client && npm run build:deploy",
+        "install:server": "pip install -r requirements.txt",
+        "dev:server": "cd ./server && python manage.py runserver",
+        "install:all": "npm run install:client && npm run install:server",
+        "start:website": "npm run build-deploy:client && npm run dev:server"
+    },
+    "keywords": [],
+    "author": "",
+    "license": "ISC"
+}
diff --git a/web/requirements.txt b/web/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a437f5fb7f77546d10425ddd6dd99211699a796
--- /dev/null
+++ b/web/requirements.txt
@@ -0,0 +1,4 @@
+Django==4.1.2
+djangorestframework==3.14.0
+django-cors-headers==3.13.0
+django-extensions==3.2.1
\ No newline at end of file
diff --git a/web/server/api/__init__.py b/web/server/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/api/admin.py b/web/server/api/admin.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c38f3f3dad51e4585f3984282c2a4bec5349c1e
--- /dev/null
+++ b/web/server/api/admin.py
@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.
diff --git a/web/server/api/apps.py b/web/server/api/apps.py
new file mode 100644
index 0000000000000000000000000000000000000000..878e7d54f787a4595a15e78fd9f65b18135aba16
--- /dev/null
+++ b/web/server/api/apps.py
@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class ApiConfig(AppConfig):
+    default_auto_field = "django.db.models.BigAutoField"
+    name = "api"
diff --git a/web/server/api/migrations/__init__.py b/web/server/api/migrations/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/api/tests.py b/web/server/api/tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ce503c2dd97ba78597f6ff6e4393132753573f6
--- /dev/null
+++ b/web/server/api/tests.py
@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.
diff --git a/web/server/api/urls.py b/web/server/api/urls.py
new file mode 100644
index 0000000000000000000000000000000000000000..e78b1cc323a50a760a355eb6cd832190a8c9b63c
--- /dev/null
+++ b/web/server/api/urls.py
@@ -0,0 +1,7 @@
+from django.urls import path, include
+from . import views
+
+urlpatterns = [
+    path("", views.api, name="api"),
+    path("video/", include("stream_video.urls")),
+]
diff --git a/web/server/api/views.py b/web/server/api/views.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f707868750ae62e2d1edb2d5b49b93f1732bf2d
--- /dev/null
+++ b/web/server/api/views.py
@@ -0,0 +1,9 @@
+from rest_framework.decorators import api_view
+from rest_framework.response import Response
+from datetime import datetime
+
+
+@api_view(["GET"])
+def api(request):
+    urls = {"name": "Quoc Bao", "dob": "10/10/2000", "time": datetime.now()}
+    return Response(urls)
diff --git a/web/server/detection/__init__.py b/web/server/detection/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/detection/bicep_curl.py b/web/server/detection/bicep_curl.py
new file mode 100644
index 0000000000000000000000000000000000000000..580f1d0dd5c9047630c16971a7c1383a04c5b67f
--- /dev/null
+++ b/web/server/detection/bicep_curl.py
@@ -0,0 +1,598 @@
+import mediapipe as mp
+import cv2
+import numpy as np
+import pandas as pd
+import pickle
+import traceback
+
+from .utils import (
+    calculate_angle,
+    extract_important_keypoints,
+    get_static_file_url,
+    get_drawing_color,
+)
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+
+class BicepPoseAnalysis:
+    def __init__(
+        self,
+        side: str,
+        stage_down_threshold: float,
+        stage_up_threshold: float,
+        peak_contraction_threshold: float,
+        loose_upper_arm_angle_threshold: float,
+        visibility_threshold: float,
+    ):
+        # Initialize thresholds
+        self.stage_down_threshold = stage_down_threshold
+        self.stage_up_threshold = stage_up_threshold
+        self.peak_contraction_threshold = peak_contraction_threshold
+        self.loose_upper_arm_angle_threshold = loose_upper_arm_angle_threshold
+        self.visibility_threshold = visibility_threshold
+
+        self.side = side
+        self.counter = 0
+        self.stage = "down"
+        self.is_visible = True
+        self.detected_errors = {
+            "LOOSE_UPPER_ARM": 0,
+            "PEAK_CONTRACTION": 0,
+        }
+
+        # Params for loose upper arm error detection
+        self.loose_upper_arm = False
+
+        # Params for peak contraction error detection
+        self.peak_contraction_angle = 1000
+
+    def get_joints(self, landmarks) -> bool:
+        """
+        Check for joints' visibility then get joints coordinate
+        """
+        side = self.side.upper()
+
+        # Check visibility
+        joints_visibility = [
+            landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].visibility,
+            landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].visibility,
+            landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].visibility,
+        ]
+
+        is_visible = all([vis > self.visibility_threshold for vis in joints_visibility])
+        self.is_visible = is_visible
+
+        if not is_visible:
+            return self.is_visible
+
+        # Get joints' coordinates
+        self.shoulder = [
+            landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].x,
+            landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].y,
+        ]
+        self.elbow = [
+            landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].x,
+            landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].y,
+        ]
+        self.wrist = [
+            landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].x,
+            landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].y,
+        ]
+
+        return self.is_visible
+
+    def analyze_pose(
+        self,
+        landmarks,
+        frame,
+        results,
+        timestamp: int,
+        lean_back_error: bool = False,
+    ):
+        """Analyze angles of an arm for error detection
+
+        Args:
+            landmarks (): MediaPipe Pose landmarks
+            frame (): OpenCV frame
+            results (): MediaPipe Pose results
+            timestamp (int): timestamp of the frame
+            lean_back_error (bool, optional): If there is an lean back error detected, ignore the analysis. Defaults to False.
+
+        Returns:
+            _type_: _description_
+        """
+        has_error = False
+        self.get_joints(landmarks)
+
+        # Cancel calculation if visibility is poor
+        if not self.is_visible:
+            return (None, None, has_error)
+
+        # * Calculate curl angle for counter
+        bicep_curl_angle = int(calculate_angle(self.shoulder, self.elbow, self.wrist))
+        if bicep_curl_angle > self.stage_down_threshold:
+            self.stage = "down"
+        elif bicep_curl_angle < self.stage_up_threshold and self.stage == "down":
+            self.stage = "up"
+            self.counter += 1
+
+        # * Calculate the angle between the upper arm (shoulder & joint) and the Y axis
+        shoulder_projection = [
+            self.shoulder[0],
+            1,
+        ]  # Represent the projection of the shoulder to the X axis
+        ground_upper_arm_angle = int(
+            calculate_angle(self.elbow, self.shoulder, shoulder_projection)
+        )
+
+        # Stop analysis if lean back error is occur
+        if lean_back_error:
+            return (bicep_curl_angle, ground_upper_arm_angle, has_error)
+
+        # * Evaluation for LOOSE UPPER ARM error
+        if ground_upper_arm_angle > self.loose_upper_arm_angle_threshold:
+            has_error = True
+            cv2.rectangle(frame, (350, 0), (600, 40), (245, 117, 16), -1)
+            cv2.putText(
+                frame,
+                "ARM ERROR",
+                (360, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                frame,
+                "LOOSE UPPER ARM",
+                (355, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Limit the saved frame
+            if not self.loose_upper_arm:
+                self.loose_upper_arm = True
+                self.detected_errors["LOOSE_UPPER_ARM"] += 1
+                results.append(
+                    {"stage": "loose upper arm", "frame": frame, "timestamp": timestamp}
+                )
+        else:
+            self.loose_upper_arm = False
+
+        # * Evaluate PEAK CONTRACTION error
+        if self.stage == "up" and bicep_curl_angle < self.peak_contraction_angle:
+            # Save peaked contraction every rep
+            self.peak_contraction_angle = bicep_curl_angle
+
+        elif self.stage == "down":
+            # * Evaluate if the peak is higher than the threshold if True, marked as an error then saved that frame
+            if (
+                self.peak_contraction_angle != 1000
+                and self.peak_contraction_angle >= self.peak_contraction_threshold
+            ):
+                cv2.rectangle(frame, (350, 0), (600, 40), (245, 117, 16), -1)
+                cv2.putText(
+                    frame,
+                    "ARM ERROR",
+                    (360, 12),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (0, 0, 0),
+                    1,
+                    cv2.LINE_AA,
+                )
+                cv2.putText(
+                    frame,
+                    "WEAK PEAK CONTRACTION",
+                    (355, 30),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (255, 255, 255),
+                    1,
+                    cv2.LINE_AA,
+                )
+
+                self.detected_errors["PEAK_CONTRACTION"] += 1
+                results.append(
+                    {
+                        "stage": "peak contraction",
+                        "frame": frame,
+                        "timestamp": timestamp,
+                    }
+                )
+                has_error = True
+
+            # Reset params
+            self.peak_contraction_angle = 1000
+
+        return (bicep_curl_angle, ground_upper_arm_angle, has_error)
+
+    def get_counter(self) -> int:
+        return self.counter
+
+    def reset(self):
+        self.counter = 0
+        self.stage = "down"
+        self.is_visible = True
+        self.detected_errors = {
+            "LOOSE_UPPER_ARM": 0,
+            "PEAK_CONTRACTION": 0,
+        }
+
+        # Params for loose upper arm error detection
+        self.loose_upper_arm = False
+
+        # Params for peak contraction error detection
+        self.peak_contraction_angle = 1000
+
+
+class BicepCurlDetection:
+    ML_MODEL_PATH = get_static_file_url("model/bicep_curl_model.pkl")
+    INPUT_SCALER = get_static_file_url("model/bicep_curl_input_scaler.pkl")
+
+    VISIBILITY_THRESHOLD = 0.65
+
+    # Params for counter
+    STAGE_UP_THRESHOLD = 100
+    STAGE_DOWN_THRESHOLD = 120
+
+    # Params to catch FULL RANGE OF MOTION error
+    PEAK_CONTRACTION_THRESHOLD = 60
+
+    # LOOSE UPPER ARM error detection
+    LOOSE_UPPER_ARM = False
+    LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40
+
+    # STANDING POSTURE error detection
+    POSTURE_ERROR_THRESHOLD = 0.95
+
+    def __init__(self) -> None:
+        self.init_important_landmarks()
+        self.load_machine_learning_model()
+
+        self.left_arm_analysis = BicepPoseAnalysis(
+            side="left",
+            stage_down_threshold=self.STAGE_DOWN_THRESHOLD,
+            stage_up_threshold=self.STAGE_UP_THRESHOLD,
+            peak_contraction_threshold=self.PEAK_CONTRACTION_THRESHOLD,
+            loose_upper_arm_angle_threshold=self.LOOSE_UPPER_ARM_ANGLE_THRESHOLD,
+            visibility_threshold=self.VISIBILITY_THRESHOLD,
+        )
+
+        self.right_arm_analysis = BicepPoseAnalysis(
+            side="right",
+            stage_down_threshold=self.STAGE_DOWN_THRESHOLD,
+            stage_up_threshold=self.STAGE_UP_THRESHOLD,
+            peak_contraction_threshold=self.PEAK_CONTRACTION_THRESHOLD,
+            loose_upper_arm_angle_threshold=self.LOOSE_UPPER_ARM_ANGLE_THRESHOLD,
+            visibility_threshold=self.VISIBILITY_THRESHOLD,
+        )
+
+        self.stand_posture = 0
+        self.previous_stand_posture = 0
+        self.results = []
+        self.has_error = False
+
+    def init_important_landmarks(self) -> None:
+        """
+        Determine Important landmarks for plank detection
+        """
+
+        self.important_landmarks = [
+            "NOSE",
+            "LEFT_SHOULDER",
+            "RIGHT_SHOULDER",
+            "RIGHT_ELBOW",
+            "LEFT_ELBOW",
+            "RIGHT_WRIST",
+            "LEFT_WRIST",
+            "LEFT_HIP",
+            "RIGHT_HIP",
+        ]
+
+        # Generate all columns of the data frame
+        self.headers = ["label"]  # Label column
+
+        for lm in self.important_landmarks:
+            self.headers += [
+                f"{lm.lower()}_x",
+                f"{lm.lower()}_y",
+                f"{lm.lower()}_z",
+                f"{lm.lower()}_v",
+            ]
+
+    def load_machine_learning_model(self) -> None:
+        """
+        Load machine learning model
+        """
+        if not self.ML_MODEL_PATH:
+            raise Exception("Cannot found plank model")
+
+        try:
+            with open(self.ML_MODEL_PATH, "rb") as f:
+                self.model = pickle.load(f)
+
+            with open(self.INPUT_SCALER, "rb") as f2:
+                self.input_scaler = pickle.load(f2)
+        except Exception as e:
+            raise Exception(f"Error loading model, {e}")
+
+    def handle_detected_results(self, video_name: str) -> tuple:
+        """
+        Save frame as evidence
+        """
+        file_name, _ = video_name.split(".")
+        save_folder = get_static_file_url("images")
+        for index, error in enumerate(self.results):
+            try:
+                image_name = f"{file_name}_{index}.jpg"
+                cv2.imwrite(f"{save_folder}/{file_name}_{index}.jpg", error["frame"])
+                self.results[index]["frame"] = image_name
+            except Exception as e:
+                print("ERROR cannot save frame: " + str(e))
+                self.results[index]["frame"] = None
+
+        return self.results, {
+            "left_counter": self.left_arm_analysis.get_counter(),
+            "right_counter": self.right_arm_analysis.get_counter(),
+        }
+
+    def clear_results(self) -> None:
+        self.stand_posture = 0
+        self.previous_stand_posture = 0
+        self.results = []
+        self.has_error = False
+
+        self.right_arm_analysis.reset()
+        self.left_arm_analysis.reset()
+
+    def detect(
+        self,
+        mp_results,
+        image,
+        timestamp: int,
+    ) -> None:
+        """Error detection
+
+        Args:
+            mp_results (): MediaPipe results
+            image (): OpenCV image
+            timestamp (int): Current time of the frame
+        """
+        self.has_error = False
+
+        try:
+            video_dimensions = [image.shape[1], image.shape[0]]
+            landmarks = mp_results.pose_landmarks.landmark
+
+            # * Model prediction for Lean-back error
+            # Extract keypoints from frame for the input
+            row = extract_important_keypoints(mp_results, self.important_landmarks)
+            X = pd.DataFrame(
+                [
+                    row,
+                ],
+                columns=self.headers[1:],
+            )
+            X = pd.DataFrame(self.input_scaler.transform(X))
+
+            # Make prediction and its probability
+            predicted_class = self.model.predict(X)[0]
+            prediction_probabilities = self.model.predict_proba(X)[0]
+            class_prediction_probability = round(
+                prediction_probabilities[np.argmax(prediction_probabilities)], 2
+            )
+
+            if class_prediction_probability >= self.POSTURE_ERROR_THRESHOLD:
+                self.stand_posture = predicted_class
+
+            # Stage management for saving results
+            if self.stand_posture == "L":
+                if self.previous_stand_posture == self.stand_posture:
+                    pass
+                elif self.previous_stand_posture != self.stand_posture:
+                    self.results.append(
+                        {
+                            "stage": "lean too far back",
+                            "frame": image,
+                            "timestamp": timestamp,
+                        }
+                    )
+
+                self.has_error = True
+
+            self.previous_stand_posture = self.stand_posture
+
+            # * Arms analysis for errors
+            # Left arm
+            (
+                left_bicep_curl_angle,
+                left_ground_upper_arm_angle,
+                left_arm_error,
+            ) = self.left_arm_analysis.analyze_pose(
+                landmarks=landmarks,
+                frame=image,
+                results=self.results,
+                timestamp=timestamp,
+                lean_back_error=(self.stand_posture == "L"),
+            )
+
+            # Right arm
+            (
+                right_bicep_curl_angle,
+                right_ground_upper_arm_angle,
+                right_arm_error,
+            ) = self.right_arm_analysis.analyze_pose(
+                landmarks=landmarks,
+                frame=image,
+                results=self.results,
+                timestamp=timestamp,
+                lean_back_error=(self.stand_posture == "L"),
+            )
+
+            self.has_error = (
+                True if (right_arm_error or left_arm_error) else self.has_error
+            )
+
+            # Visualization
+            # Draw landmarks and connections
+            landmark_color, connection_color = get_drawing_color(self.has_error)
+            mp_drawing.draw_landmarks(
+                image,
+                mp_results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(
+                    color=landmark_color, thickness=2, circle_radius=2
+                ),
+                mp_drawing.DrawingSpec(
+                    color=connection_color, thickness=2, circle_radius=1
+                ),
+            )
+
+            # Status box
+            cv2.rectangle(image, (0, 0), (350, 40), (245, 117, 16), -1)
+
+            # Display probability
+            cv2.putText(
+                image,
+                "RIGHT",
+                (15, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                str(self.right_arm_analysis.counter)
+                if self.right_arm_analysis.is_visible
+                else "UNK",
+                (10, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Display Left Counter
+            cv2.putText(
+                image,
+                "LEFT",
+                (95, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                str(self.left_arm_analysis.counter)
+                if self.left_arm_analysis.is_visible
+                else "UNK",
+                (100, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Lean back error
+            cv2.putText(
+                image,
+                "Lean-Too-Far-Back",
+                (165, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                str("ERROR" if self.stand_posture == "L" else "CORRECT")
+                + f", {predicted_class}, {class_prediction_probability}",
+                (160, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # * Visualize angles
+            # Visualize LEFT arm calculated angles
+            if self.left_arm_analysis.is_visible:
+                cv2.putText(
+                    image,
+                    str(left_bicep_curl_angle),
+                    tuple(
+                        np.multiply(
+                            self.left_arm_analysis.elbow, video_dimensions
+                        ).astype(int)
+                    ),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (255, 255, 255),
+                    1,
+                    cv2.LINE_AA,
+                )
+                cv2.putText(
+                    image,
+                    str(left_ground_upper_arm_angle),
+                    tuple(
+                        np.multiply(
+                            self.left_arm_analysis.shoulder, video_dimensions
+                        ).astype(int)
+                    ),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (255, 255, 255),
+                    1,
+                    cv2.LINE_AA,
+                )
+
+            # Visualize RIGHT arm calculated angles
+            if self.right_arm_analysis.is_visible:
+                cv2.putText(
+                    image,
+                    str(right_bicep_curl_angle),
+                    tuple(
+                        np.multiply(
+                            self.right_arm_analysis.elbow, video_dimensions
+                        ).astype(int)
+                    ),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (255, 255, 0),
+                    1,
+                    cv2.LINE_AA,
+                )
+                cv2.putText(
+                    image,
+                    str(right_ground_upper_arm_angle),
+                    tuple(
+                        np.multiply(
+                            self.right_arm_analysis.shoulder, video_dimensions
+                        ).astype(int)
+                    ),
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    0.5,
+                    (255, 255, 0),
+                    1,
+                    cv2.LINE_AA,
+                )
+
+        except Exception as e:
+            traceback.print_exc()
+            raise e
diff --git a/web/server/detection/lunge.py b/web/server/detection/lunge.py
new file mode 100644
index 0000000000000000000000000000000000000000..decf227004250fc0c7056e8b1a0afefda2f76c20
--- /dev/null
+++ b/web/server/detection/lunge.py
@@ -0,0 +1,452 @@
+import cv2, pickle
+import mediapipe as mp
+import numpy as np
+import pandas as pd
+
+from .utils import (
+    calculate_angle,
+    extract_important_keypoints,
+    get_static_file_url,
+    get_drawing_color,
+)
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+
+def analyze_knee_angle(
+    mp_results,
+    stage: str,
+    angle_thresholds: list,
+    knee_over_toe: bool = False,
+    draw_to_image: tuple = None,
+) -> dict:
+    """Calculate angle of each knee while performer at the DOWN position
+
+    Args:
+        mp_results (): MediaPipe Pose results
+        stage (str): stage of the exercise
+        angle_thresholds (list): lower and upper limits for the knee angles
+        knee_over_toe (bool): if knee_over_toe error occur, ignore knee angles. Default to False
+        draw_to_image (tuple, optional): Contains an OpenCV frame and its dimension. Defaults to None.
+
+    Returns:
+        dict: Statistic from analyze knee angles
+    """
+    results = {
+        "error": None,
+        "right": {"error": None, "angle": None},
+        "left": {"error": None, "angle": None},
+    }
+
+    landmarks = mp_results.pose_landmarks.landmark
+
+    # Calculate right knee angle
+    right_hip = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y,
+    ]
+    right_knee = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y,
+    ]
+    right_ankle = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y,
+    ]
+    results["right"]["angle"] = calculate_angle(right_hip, right_knee, right_ankle)
+
+    # Calculate left knee angle
+    left_hip = [
+        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y,
+    ]
+    left_knee = [
+        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y,
+    ]
+    left_ankle = [
+        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y,
+    ]
+    results["left"]["angle"] = calculate_angle(left_hip, left_knee, left_ankle)
+
+    # Draw to image
+    if draw_to_image is not None and stage != "down":
+        (image, video_dimensions) = draw_to_image
+
+        # Visualize angles
+        cv2.putText(
+            image,
+            str(int(results["right"]["angle"])),
+            tuple(np.multiply(right_knee, video_dimensions).astype(int)),
+            cv2.FONT_HERSHEY_COMPLEX,
+            0.5,
+            (255, 255, 255),
+            1,
+            cv2.LINE_AA,
+        )
+        cv2.putText(
+            image,
+            str(int(results["left"]["angle"])),
+            tuple(np.multiply(left_knee, video_dimensions).astype(int)),
+            cv2.FONT_HERSHEY_COMPLEX,
+            0.5,
+            (255, 255, 255),
+            1,
+            cv2.LINE_AA,
+        )
+
+    if stage != "down":
+        return results
+
+    # Ignore checking for knee angle error if knee_over_toe error occur
+    if knee_over_toe:
+        return results
+
+    # Evaluation
+    results["error"] = False
+
+    if angle_thresholds[0] <= results["right"]["angle"] <= angle_thresholds[1]:
+        results["right"]["error"] = False
+    else:
+        results["right"]["error"] = True
+        results["error"] = True
+
+    if angle_thresholds[0] <= results["left"]["angle"] <= angle_thresholds[1]:
+        results["left"]["error"] = False
+    else:
+        results["left"]["error"] = True
+        results["error"] = True
+
+    # Draw to image
+    if draw_to_image is not None:
+        (image, video_dimensions) = draw_to_image
+
+        if results["error"]:
+            cv2.rectangle(image, (0, 50), (120, 100), (245, 117, 16), -1)
+            cv2.putText(
+                image,
+                "KNEE ANGLE ERROR",
+                (10, 62),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.3,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                "LEFT KNEE" if results["left"]["error"] else "RIGHT KNEE",
+                (10, 82),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.3,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+        right_color = (255, 255, 255) if not results["right"]["error"] else (0, 0, 255)
+        left_color = (255, 255, 255) if not results["left"]["error"] else (0, 0, 255)
+
+        # Visualize angles
+        cv2.putText(
+            image,
+            str(int(results["right"]["angle"])),
+            tuple(np.multiply(right_knee, video_dimensions).astype(int)),
+            cv2.FONT_HERSHEY_COMPLEX,
+            0.5,
+            right_color,
+            1,
+            cv2.LINE_AA,
+        )
+        cv2.putText(
+            image,
+            str(int(results["left"]["angle"])),
+            tuple(np.multiply(left_knee, video_dimensions).astype(int)),
+            cv2.FONT_HERSHEY_COMPLEX,
+            0.5,
+            left_color,
+            1,
+            cv2.LINE_AA,
+        )
+
+    return results
+
+
+class LungeDetection:
+    STAGE_ML_MODEL_PATH = get_static_file_url("model/lunge_stage_model.pkl")
+    ERR_ML_MODEL_PATH = get_static_file_url("model/lunge_err_model.pkl")
+    INPUT_SCALER_PATH = get_static_file_url("model/lunge_input_scaler.pkl")
+
+    PREDICTION_PROB_THRESHOLD = 0.8
+    KNEE_ANGLE_THRESHOLD = [60, 125]
+
+    def __init__(self) -> None:
+        self.init_important_landmarks()
+        self.load_machine_learning_model()
+
+        self.current_stage = ""
+        self.counter = 0
+        self.results = []
+        self.has_error = False
+
+    def init_important_landmarks(self) -> None:
+        """
+        Determine Important landmarks for lunge detection
+        """
+
+        self.important_landmarks = [
+            "NOSE",
+            "LEFT_SHOULDER",
+            "RIGHT_SHOULDER",
+            "LEFT_HIP",
+            "RIGHT_HIP",
+            "LEFT_KNEE",
+            "RIGHT_KNEE",
+            "LEFT_ANKLE",
+            "RIGHT_ANKLE",
+            "LEFT_HEEL",
+            "RIGHT_HEEL",
+            "LEFT_FOOT_INDEX",
+            "RIGHT_FOOT_INDEX",
+        ]
+
+        # Generate all columns of the data frame
+        self.headers = ["label"]  # Label column
+
+        for lm in self.important_landmarks:
+            self.headers += [
+                f"{lm.lower()}_x",
+                f"{lm.lower()}_y",
+                f"{lm.lower()}_z",
+                f"{lm.lower()}_v",
+            ]
+
+    def load_machine_learning_model(self) -> None:
+        """
+        Load machine learning model
+        """
+        if (
+            not self.STAGE_ML_MODEL_PATH
+            or not self.INPUT_SCALER_PATH
+            or not self.ERR_ML_MODEL_PATH
+        ):
+            raise Exception("Cannot found lunge files for prediction")
+
+        try:
+            with open(self.ERR_ML_MODEL_PATH, "rb") as f:
+                self.err_model = pickle.load(f)
+
+            with open(self.STAGE_ML_MODEL_PATH, "rb") as f:
+                self.stage_model = pickle.load(f)
+
+            with open(self.INPUT_SCALER_PATH, "rb") as f2:
+                self.input_scaler = pickle.load(f2)
+        except Exception as e:
+            raise Exception(f"Error loading model, {e}")
+
+    def handle_detected_results(self, video_name: str) -> tuple:
+        """
+        Save frame as evidence
+        """
+        file_name, _ = video_name.split(".")
+        save_folder = get_static_file_url("images")
+        for index, error in enumerate(self.results):
+            try:
+                image_name = f"{file_name}_{index}.jpg"
+                cv2.imwrite(f"{save_folder}/{file_name}_{index}.jpg", error["frame"])
+                self.results[index]["frame"] = image_name
+            except Exception as e:
+                print("ERROR cannot save frame: " + str(e))
+                self.results[index]["frame"] = None
+
+        return self.results, self.counter
+
+    def clear_results(self) -> None:
+        self.results = []
+        self.counter = 0
+        self.current_stage = ""
+        self.has_error = False
+
+    def detect(self, mp_results, image, timestamp) -> None:
+        """
+        Make Lunge Errors detection
+        """
+        try:
+            video_dimensions = [image.shape[1], image.shape[0]]
+
+            # * Model prediction for LUNGE counter
+            # Extract keypoints from frame for the input
+            row = extract_important_keypoints(mp_results, self.important_landmarks)
+            X = pd.DataFrame([row], columns=self.headers[1:])
+            X = pd.DataFrame(self.input_scaler.transform(X))
+
+            # Make prediction and its probability
+            stage_predicted_class = self.stage_model.predict(X)[0]
+            stage_prediction_probabilities = self.stage_model.predict_proba(X)[0]
+            stage_prediction_probability = round(
+                stage_prediction_probabilities[stage_prediction_probabilities.argmax()],
+                2,
+            )
+
+            # Evaluate stage prediction for counter
+            if (
+                stage_predicted_class == "I"
+                and stage_prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+            ):
+                self.current_stage = "init"
+            elif (
+                stage_predicted_class == "M"
+                and stage_prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+            ):
+                self.current_stage = "mid"
+            elif (
+                stage_predicted_class == "D"
+                and stage_prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+            ):
+                if self.current_stage in ["init", "mid"]:
+                    self.counter += 1
+
+                self.current_stage = "down"
+
+            # Check out errors from a rep to reduce repeated warning
+            errors_from_this_rep = map(
+                lambda el: el["stage"],
+                filter(lambda el: el["counter"] == self.counter, self.results),
+            )
+
+            # Analyze lunge pose
+            # Knee over toe
+            k_o_t_error = None
+            err_predicted_class = None
+            err_prediction_probabilities = None
+            err_prediction_probability = None
+            if self.current_stage == "down":
+                err_predicted_class = self.err_model.predict(X)[0]
+                err_prediction_probabilities = self.err_model.predict_proba(X)[0]
+                err_prediction_probability = round(
+                    err_prediction_probabilities[err_prediction_probabilities.argmax()],
+                    2,
+                )
+
+                if (
+                    err_predicted_class == "L"
+                    and err_prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+                ):
+                    k_o_t_error = "Incorrect"
+                    self.has_error = True
+
+                    # Limit save error frames saved in a rep
+                    if (
+                        len(self.results) == 0
+                        or "knee over toe" not in errors_from_this_rep
+                    ):
+                        self.results.append(
+                            {
+                                "stage": f"knee over toe",
+                                "frame": image,
+                                "timestamp": timestamp,
+                                "counter": self.counter,
+                            }
+                        )
+
+                elif (
+                    err_predicted_class == "C"
+                    and err_prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+                ):
+                    k_o_t_error = "Correct"
+                    self.has_error = False
+            else:
+                self.has_error = False
+
+            # Analyze lunge pose
+            # * Knee angle
+            analyzed_results = analyze_knee_angle(
+                mp_results=mp_results,
+                stage=self.current_stage,
+                angle_thresholds=self.KNEE_ANGLE_THRESHOLD,
+                knee_over_toe=(k_o_t_error == "Incorrect"),
+                draw_to_image=(image, video_dimensions),
+            )
+
+            # Stage management for saving results
+            self.has_error = (
+                analyzed_results["error"] if not self.has_error else self.has_error
+            )
+            if analyzed_results["error"]:
+                # Limit save error frames saved in a rep
+                if len(self.results) == 0 or "knee angle" not in errors_from_this_rep:
+                    self.results.append(
+                        {
+                            "stage": f"knee angle",
+                            "frame": image,
+                            "timestamp": timestamp,
+                            "counter": self.counter,
+                        }
+                    )
+
+            # Visualization
+            # Draw landmarks and connections
+            landmark_color, connection_color = get_drawing_color(self.has_error)
+            mp_drawing.draw_landmarks(
+                image,
+                mp_results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(
+                    color=landmark_color, thickness=2, circle_radius=2
+                ),
+                mp_drawing.DrawingSpec(
+                    color=connection_color, thickness=2, circle_radius=1
+                ),
+            )
+
+            # Status box
+            cv2.rectangle(image, (0, 0), (325, 40), (245, 117, 16), -1)
+
+            # Display Stage prediction for count
+            cv2.putText(
+                image,
+                "COUNT",
+                (10, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                f'{str(self.counter)}, {stage_predicted_class.split(" ")[0]}, {str(stage_prediction_probability)}',
+                (5, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Display KNEE_OVER_TOE error prediction
+            cv2.putText(
+                image,
+                "KNEE_OVER_TOE",
+                (145, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                f"{err_predicted_class}, {err_prediction_probability}, {k_o_t_error}",
+                (135, 30),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+        except Exception as e:
+            print(f"Error while detecting lunge errors: {e}")
diff --git a/web/server/detection/main.py b/web/server/detection/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..d85ccea12561919c26c66a618c42c620ab387ef0
--- /dev/null
+++ b/web/server/detection/main.py
@@ -0,0 +1,169 @@
+import mediapipe as mp
+import cv2
+from django.conf import settings
+
+from .plank import PlankDetection
+from .bicep_curl import BicepCurlDetection
+from .squat import SquatDetection
+from .lunge import LungeDetection
+from .utils import rescale_frame
+
+# Drawing helpers
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+EXERCISE_DETECTIONS = None
+
+
+def load_machine_learning_models():
+    """Load all machine learning models"""
+    global EXERCISE_DETECTIONS
+
+    if EXERCISE_DETECTIONS is not None:
+        return
+
+    print("Loading ML models ...")
+    EXERCISE_DETECTIONS = {
+        "plank": PlankDetection(),
+        "bicep_curl": BicepCurlDetection(),
+        "squat": SquatDetection(),
+        "lunge": LungeDetection(),
+    }
+
+
+def pose_detection(
+    video_file_path: str, video_name_to_save: str, rescale_percent: float = 40
+):
+    """Pose detection with MediaPipe Pose
+
+    Args:
+        video_file_path (str): path to video
+        video_name_to_save (str): path to save analyzed video
+        rescale_percent (float, optional): Percentage to scale back from the original video size. Defaults to 40.
+
+    """
+    cap = cv2.VideoCapture(video_file_path)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) * rescale_percent / 100)
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * rescale_percent / 100)
+    size = (width, height)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+    fourcc = cv2.VideoWriter_fourcc(*"avc1")
+    save_to_path = f"{settings.MEDIA_ROOT}/{video_name_to_save}"
+    out = cv2.VideoWriter(save_to_path, fourcc, fps, size)
+
+    print("PROCESSING VIDEO ...")
+    with mp_pose.Pose(
+        min_detection_confidence=0.8, min_tracking_confidence=0.8
+    ) as pose:
+        while cap.isOpened():
+            ret, image = cap.read()
+
+            if not ret:
+                break
+
+            image = rescale_frame(image, rescale_percent)
+
+            # Recolor image from BGR to RGB for mediapipe
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            image.flags.writeable = False
+
+            results = pose.process(image)
+
+            # Recolor image from BGR to RGB for mediapipe
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            mp_drawing.draw_landmarks(
+                image,
+                results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(
+                    color=(244, 117, 66), thickness=2, circle_radius=2
+                ),
+                mp_drawing.DrawingSpec(
+                    color=(245, 66, 230), thickness=2, circle_radius=1
+                ),
+            )
+
+            out.write(image)
+
+    print(f"PROCESSED, save to {save_to_path}.")
+    return
+
+
+def exercise_detection(
+    video_file_path: str,
+    video_name_to_save: str,
+    exercise_type: str,
+    rescale_percent: float = 40,
+) -> dict:
+    """Analyzed Exercise Video
+
+    Args:
+        video_file_path (str): path to video
+        video_name_to_save (str): path to save analyzed video
+        exercise_type (str): exercise type
+        rescale_percent (float, optional): Percentage to scale back from the original video size. Defaults to 40.
+
+    Raises:
+        Exception: Not supported exercise type
+
+    Returns:
+        dict: Dictionary of analyzed stats from the video
+    """
+    exercise_detection = EXERCISE_DETECTIONS.get(exercise_type)
+    if not exercise_detection:
+        raise Exception("Not supported exercise.")
+
+    cap = cv2.VideoCapture(video_file_path)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) * rescale_percent / 100)
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * rescale_percent / 100)
+    size = (width, height)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_count = 0
+
+    fourcc = cv2.VideoWriter_fourcc(*"avc1")
+    saved_path = f"{settings.MEDIA_ROOT}/{video_name_to_save}"
+    out = cv2.VideoWriter(saved_path, fourcc, fps, size)
+
+    print("PROCESSING VIDEO ...")
+    with mp_pose.Pose(
+        min_detection_confidence=0.8, min_tracking_confidence=0.8
+    ) as pose:
+        while cap.isOpened():
+            ret, image = cap.read()
+
+            if not ret:
+                break
+
+            # Calculate timestamp
+            frame_count += 1
+            timestamp = int(frame_count / fps)
+
+            image = rescale_frame(image, rescale_percent)
+
+            # Recolor image from BGR to RGB for mediapipe
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            image.flags.writeable = False
+
+            results = pose.process(image)
+
+            # Recolor image from BGR to RGB for mediapipe
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            if results.pose_landmarks:
+                exercise_detection.detect(
+                    mp_results=results, image=image, timestamp=timestamp
+                )
+
+            out.write(image)
+
+    print(f"PROCESSED. Save path: {saved_path}")
+
+    processed_results = exercise_detection.handle_detected_results(
+        video_name=video_name_to_save
+    )
+    exercise_detection.clear_results()
+    return processed_results
diff --git a/web/server/detection/plank.py b/web/server/detection/plank.py
new file mode 100644
index 0000000000000000000000000000000000000000..96168e68030109480cbdfb0a7cadc10b7deb9a13
--- /dev/null
+++ b/web/server/detection/plank.py
@@ -0,0 +1,216 @@
+import cv2
+import numpy as np
+import pandas as pd
+import pickle
+import mediapipe as mp
+
+from .utils import extract_important_keypoints, get_static_file_url, get_drawing_color
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+
+class PlankDetection:
+    ML_MODEL_PATH = get_static_file_url("model/plank_model.pkl")
+    INPUT_SCALER_PATH = get_static_file_url("model/plank_input_scaler.pkl")
+    PREDICTION_PROBABILITY_THRESHOLD = 0.6
+
+    def __init__(self) -> None:
+        self.init_important_landmarks()
+        self.load_machine_learning_model()
+
+        self.previous_stage = "unknown"
+        self.results = []
+        self.has_error = False
+
+    def init_important_landmarks(self) -> None:
+        """
+        Determine Important landmarks for plank detection
+        """
+
+        self.important_landmarks = [
+            "NOSE",
+            "LEFT_SHOULDER",
+            "RIGHT_SHOULDER",
+            "LEFT_ELBOW",
+            "RIGHT_ELBOW",
+            "LEFT_WRIST",
+            "RIGHT_WRIST",
+            "LEFT_HIP",
+            "RIGHT_HIP",
+            "LEFT_KNEE",
+            "RIGHT_KNEE",
+            "LEFT_ANKLE",
+            "RIGHT_ANKLE",
+            "LEFT_HEEL",
+            "RIGHT_HEEL",
+            "LEFT_FOOT_INDEX",
+            "RIGHT_FOOT_INDEX",
+        ]
+
+        # Generate all columns of the data frame
+        self.headers = ["label"]  # Label column
+
+        for lm in self.important_landmarks:
+            self.headers += [
+                f"{lm.lower()}_x",
+                f"{lm.lower()}_y",
+                f"{lm.lower()}_z",
+                f"{lm.lower()}_v",
+            ]
+
+    def load_machine_learning_model(self) -> None:
+        """
+        Load machine learning model
+        """
+        if not self.ML_MODEL_PATH or not self.INPUT_SCALER_PATH:
+            raise Exception("Cannot found plank model file or input scaler file")
+
+        try:
+            with open(self.ML_MODEL_PATH, "rb") as f:
+                self.model = pickle.load(f)
+            with open(self.INPUT_SCALER_PATH, "rb") as f2:
+                self.input_scaler = pickle.load(f2)
+        except Exception as e:
+            raise Exception(f"Error loading model, {e}")
+
+    def handle_detected_results(self, video_name: str) -> None:
+        """
+        Save frame as evidence
+        """
+        file_name, _ = video_name.split(".")
+        save_folder = get_static_file_url("images")
+        for index, error in enumerate(self.results):
+            try:
+                image_name = f"{file_name}_{index}.jpg"
+                cv2.imwrite(f"{save_folder}/{file_name}_{index}.jpg", error["frame"])
+                self.results[index]["frame"] = image_name
+            except Exception as e:
+                print("ERROR cannot save frame: " + str(e))
+                self.results[index]["frame"] = None
+
+        return self.results, self.previous_stage
+
+    def clear_results(self) -> None:
+        self.previous_stage = "unknown"
+        self.results = []
+        self.has_error = False
+
+    def detect(self, mp_results, image, timestamp) -> None:
+        """
+        Make Plank Errors detection
+        """
+        try:
+            # Extract keypoints from frame for the input
+            row = extract_important_keypoints(mp_results, self.important_landmarks)
+            X = pd.DataFrame([row], columns=self.headers[1:])
+            X = pd.DataFrame(self.input_scaler.transform(X))
+
+            # Make prediction and its probability
+            predicted_class = self.model.predict(X)[0]
+            prediction_probability = self.model.predict_proba(X)[0]
+
+            # Evaluate model prediction
+            if (
+                predicted_class == "C"
+                and prediction_probability[prediction_probability.argmax()]
+                >= self.PREDICTION_PROBABILITY_THRESHOLD
+            ):
+                current_stage = "correct"
+            elif (
+                predicted_class == "L"
+                and prediction_probability[prediction_probability.argmax()]
+                >= self.PREDICTION_PROBABILITY_THRESHOLD
+            ):
+                current_stage = "low back"
+            elif (
+                predicted_class == "H"
+                and prediction_probability[prediction_probability.argmax()]
+                >= self.PREDICTION_PROBABILITY_THRESHOLD
+            ):
+                current_stage = "high back"
+            else:
+                current_stage = "unknown"
+
+            # Stage management for saving results
+            if current_stage in ["low back", "high back"]:
+                # Stage not change
+                if self.previous_stage == current_stage:
+                    pass
+                # Stage from correct to error
+                elif self.previous_stage != current_stage:
+                    self.results.append(
+                        {"stage": current_stage, "frame": image, "timestamp": timestamp}
+                    )
+                    self.has_error = True
+            else:
+                self.has_error = False
+
+            self.previous_stage = current_stage
+
+            # Visualization
+            # Draw landmarks and connections
+            landmark_color, connection_color = get_drawing_color(self.has_error)
+            mp_drawing.draw_landmarks(
+                image,
+                mp_results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(
+                    color=landmark_color, thickness=2, circle_radius=2
+                ),
+                mp_drawing.DrawingSpec(
+                    color=connection_color, thickness=2, circle_radius=1
+                ),
+            )
+
+            # Status box
+            cv2.rectangle(image, (0, 0), (250, 60), (245, 117, 16), -1)
+
+            # Display probability
+            cv2.putText(
+                image,
+                "PROB",
+                (15, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                str(
+                    round(prediction_probability[np.argmax(prediction_probability)], 2)
+                ),
+                (10, 40),
+                cv2.FONT_HERSHEY_COMPLEX,
+                1,
+                (255, 255, 255),
+                2,
+                cv2.LINE_AA,
+            )
+
+            # Display class
+            cv2.putText(
+                image,
+                "CLASS",
+                (95, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                current_stage,
+                (90, 40),
+                cv2.FONT_HERSHEY_COMPLEX,
+                1,
+                (255, 255, 255),
+                2,
+                cv2.LINE_AA,
+            )
+
+        except Exception as e:
+            raise Exception(f"Error while detecting plank errors: {e}")
diff --git a/web/server/detection/squat.py b/web/server/detection/squat.py
new file mode 100644
index 0000000000000000000000000000000000000000..03b6ae24f77d700cfe5c0cc8498a2da88cece12b
--- /dev/null
+++ b/web/server/detection/squat.py
@@ -0,0 +1,444 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+import pandas as pd
+import pickle
+
+from .utils import (
+    calculate_distance,
+    extract_important_keypoints,
+    get_static_file_url,
+    get_drawing_color,
+)
+
+mp_pose = mp.solutions.pose
+mp_drawing = mp.solutions.drawing_utils
+
+
+def analyze_foot_knee_placement(
+    results,
+    stage: str,
+    foot_shoulder_ratio_thresholds: list,
+    knee_foot_ratio_thresholds: dict,
+    visibility_threshold: int,
+) -> dict:
+    """
+    Calculate the ratio between the foot and shoulder for FOOT PLACEMENT analysis
+
+    Calculate the ratio between the knee and foot for KNEE PLACEMENT analysis
+
+    Return result explanation:
+        -1: Unknown result due to poor visibility
+        0: Correct knee placement
+        1: Placement too tight
+        2: Placement too wide
+    """
+    analyzed_results = {
+        "foot_placement": -1,
+        "knee_placement": -1,
+    }
+
+    landmarks = results.pose_landmarks.landmark
+
+    # * Visibility check of important landmarks for foot placement analysis
+    left_foot_index_vis = landmarks[
+        mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value
+    ].visibility
+    right_foot_index_vis = landmarks[
+        mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value
+    ].visibility
+
+    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility
+    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility
+
+    # If visibility of any keypoints is low cancel the analysis
+    if (
+        left_foot_index_vis < visibility_threshold
+        or right_foot_index_vis < visibility_threshold
+        or left_knee_vis < visibility_threshold
+        or right_knee_vis < visibility_threshold
+    ):
+        return analyzed_results
+
+    # * Calculate shoulder width
+    left_shoulder = [
+        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y,
+    ]
+    right_shoulder = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y,
+    ]
+    shoulder_width = calculate_distance(left_shoulder, right_shoulder)
+
+    # * Calculate 2-foot width
+    left_foot_index = [
+        landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y,
+    ]
+    right_foot_index = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y,
+    ]
+    foot_width = calculate_distance(left_foot_index, right_foot_index)
+
+    # * Calculate foot and shoulder ratio
+    foot_shoulder_ratio = round(foot_width / shoulder_width, 1)
+
+    # * Analyze FOOT PLACEMENT
+    min_ratio_foot_shoulder, max_ratio_foot_shoulder = foot_shoulder_ratio_thresholds
+    if min_ratio_foot_shoulder <= foot_shoulder_ratio <= max_ratio_foot_shoulder:
+        analyzed_results["foot_placement"] = 0
+    elif foot_shoulder_ratio < min_ratio_foot_shoulder:
+        analyzed_results["foot_placement"] = 1
+    elif foot_shoulder_ratio > max_ratio_foot_shoulder:
+        analyzed_results["foot_placement"] = 2
+
+    # * Visibility check of important landmarks for knee placement analysis
+    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility
+    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility
+
+    # If visibility of any keypoints is low cancel the analysis
+    if left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold:
+        print("Cannot see foot")
+        return analyzed_results
+
+    # * Calculate 2 knee width
+    left_knee = [
+        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
+        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y,
+    ]
+    right_knee = [
+        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
+        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y,
+    ]
+    knee_width = calculate_distance(left_knee, right_knee)
+
+    # * Calculate foot and shoulder ratio
+    knee_foot_ratio = round(knee_width / foot_width, 1)
+
+    # * Analyze KNEE placement
+    up_min_ratio_knee_foot, up_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(
+        "up"
+    )
+    (
+        middle_min_ratio_knee_foot,
+        middle_max_ratio_knee_foot,
+    ) = knee_foot_ratio_thresholds.get("middle")
+    down_min_ratio_knee_foot, down_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(
+        "down"
+    )
+
+    if stage == "up":
+        if up_min_ratio_knee_foot <= knee_foot_ratio <= up_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 0
+        elif knee_foot_ratio < up_min_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 1
+        elif knee_foot_ratio > up_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 2
+    elif stage == "middle":
+        if middle_min_ratio_knee_foot <= knee_foot_ratio <= middle_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 0
+        elif knee_foot_ratio < middle_min_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 1
+        elif knee_foot_ratio > middle_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 2
+    elif stage == "down":
+        if down_min_ratio_knee_foot <= knee_foot_ratio <= down_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 0
+        elif knee_foot_ratio < down_min_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 1
+        elif knee_foot_ratio > down_max_ratio_knee_foot:
+            analyzed_results["knee_placement"] = 2
+
+    return analyzed_results
+
+
+class SquatDetection:
+    ML_MODEL_PATH = get_static_file_url("model/squat_model.pkl")
+
+    PREDICTION_PROB_THRESHOLD = 0.7
+    VISIBILITY_THRESHOLD = 0.6
+    FOOT_SHOULDER_RATIO_THRESHOLDS = [1.2, 2.8]
+    KNEE_FOOT_RATIO_THRESHOLDS = {
+        "up": [0.5, 1.0],
+        "middle": [0.7, 1.0],
+        "down": [0.7, 1.1],
+    }
+
+    def __init__(self) -> None:
+        self.init_important_landmarks()
+        self.load_machine_learning_model()
+
+        self.current_stage = ""
+        self.previous_stage = {
+            "feet": "",
+            "knee": "",
+        }
+        self.counter = 0
+        self.results = []
+        self.has_error = False
+
+    def init_important_landmarks(self) -> None:
+        """
+        Determine Important landmarks for squat detection
+        """
+
+        self.important_landmarks = [
+            "NOSE",
+            "LEFT_SHOULDER",
+            "RIGHT_SHOULDER",
+            "LEFT_HIP",
+            "RIGHT_HIP",
+            "LEFT_KNEE",
+            "RIGHT_KNEE",
+            "LEFT_ANKLE",
+            "RIGHT_ANKLE",
+        ]
+
+        # Generate all columns of the data frame
+        self.headers = ["label"]  # Label column
+
+        for lm in self.important_landmarks:
+            self.headers += [
+                f"{lm.lower()}_x",
+                f"{lm.lower()}_y",
+                f"{lm.lower()}_z",
+                f"{lm.lower()}_v",
+            ]
+
+    def load_machine_learning_model(self) -> None:
+        """
+        Load machine learning model
+        """
+        if not self.ML_MODEL_PATH:
+            raise Exception("Cannot found squat model")
+
+        try:
+            with open(self.ML_MODEL_PATH, "rb") as f:
+                self.model = pickle.load(f)
+        except Exception as e:
+            raise Exception(f"Error loading model, {e}")
+
+    def handle_detected_results(self, video_name: str) -> tuple:
+        """
+        Save error frame as evidence
+        """
+        file_name, _ = video_name.split(".")
+        save_folder = get_static_file_url("images")
+        for index, error in enumerate(self.results):
+            try:
+                image_name = f"{file_name}_{index}.jpg"
+                cv2.imwrite(f"{save_folder}/{file_name}_{index}.jpg", error["frame"])
+                self.results[index]["frame"] = image_name
+            except Exception as e:
+                print("ERROR cannot save frame: " + str(e))
+                self.results[index]["frame"] = None
+
+        return self.results, self.counter
+
+    def clear_results(self) -> None:
+        self.current_stage = ""
+        self.previous_stage = {
+            "feet": "",
+            "knee": "",
+        }
+        self.counter = 0
+        self.results = []
+        self.has_error = False
+
+    def detect(self, mp_results, image, timestamp) -> None:
+        """
+        Make Squat Errors detection
+        """
+        try:
+            # * Model prediction for SQUAT counter
+            # Extract keypoints from frame for the input
+            row = extract_important_keypoints(mp_results, self.important_landmarks)
+            X = pd.DataFrame([row], columns=self.headers[1:])
+
+            # Make prediction and its probability
+            predicted_class = self.model.predict(X)[0]
+            prediction_probabilities = self.model.predict_proba(X)[0]
+            prediction_probability = round(
+                prediction_probabilities[prediction_probabilities.argmax()], 2
+            )
+
+            # Evaluate model prediction
+            if (
+                predicted_class == "down"
+                and prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+            ):
+                self.current_stage = "down"
+            elif (
+                self.current_stage == "down"
+                and predicted_class == "up"
+                and prediction_probability >= self.PREDICTION_PROB_THRESHOLD
+            ):
+                self.current_stage = "up"
+                self.counter += 1
+
+            # Analyze squat pose
+            analyzed_results = analyze_foot_knee_placement(
+                results=mp_results,
+                stage=self.current_stage,
+                foot_shoulder_ratio_thresholds=self.FOOT_SHOULDER_RATIO_THRESHOLDS,
+                knee_foot_ratio_thresholds=self.KNEE_FOOT_RATIO_THRESHOLDS,
+                visibility_threshold=self.VISIBILITY_THRESHOLD,
+            )
+
+            foot_placement_evaluation = analyzed_results["foot_placement"]
+            knee_placement_evaluation = analyzed_results["knee_placement"]
+
+            # * Evaluate FEET PLACEMENT error
+            if foot_placement_evaluation == -1:
+                feet_placement = "unknown"
+            elif foot_placement_evaluation == 0:
+                feet_placement = "correct"
+            elif foot_placement_evaluation == 1:
+                feet_placement = "too tight"
+            elif foot_placement_evaluation == 2:
+                feet_placement = "too wide"
+
+            # * Evaluate KNEE PLACEMENT error
+            if feet_placement == "correct":
+                if knee_placement_evaluation == -1:
+                    knee_placement = "unknown"
+                elif knee_placement_evaluation == 0:
+                    knee_placement = "correct"
+                elif knee_placement_evaluation == 1:
+                    knee_placement = "too tight"
+                elif knee_placement_evaluation == 2:
+                    knee_placement = "too wide"
+            else:
+                knee_placement = "unknown"
+
+            # Stage management for saving results
+            # * Feet placement
+            if feet_placement in ["too tight", "too wide"]:
+                # Stage not change
+                if self.previous_stage["feet"] == feet_placement:
+                    pass
+                # Stage from correct to error
+                elif self.previous_stage["feet"] != feet_placement:
+                    self.results.append(
+                        {
+                            "stage": f"feet {feet_placement}",
+                            "frame": image,
+                            "timestamp": timestamp,
+                        }
+                    )
+
+                self.previous_stage["feet"] = feet_placement
+
+            # * Knee placement
+            if knee_placement in ["too tight", "too wide"]:
+                # Stage not change
+                if self.previous_stage["knee"] == knee_placement:
+                    pass
+                # Stage from correct to error
+                elif self.previous_stage["knee"] != knee_placement:
+                    self.results.append(
+                        {
+                            "stage": f"knee {knee_placement}",
+                            "frame": image,
+                            "timestamp": timestamp,
+                        }
+                    )
+
+                self.previous_stage["knee"] = knee_placement
+
+            if feet_placement in ["too tight", "too wide"] or knee_placement in [
+                "too tight",
+                "too wide",
+            ]:
+                self.has_error = True
+            else:
+                self.has_error = False
+
+            # Visualization
+            # Draw landmarks and connections
+            landmark_color, connection_color = get_drawing_color(self.has_error)
+            mp_drawing.draw_landmarks(
+                image,
+                mp_results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(
+                    color=landmark_color, thickness=2, circle_radius=2
+                ),
+                mp_drawing.DrawingSpec(
+                    color=connection_color, thickness=2, circle_radius=1
+                ),
+            )
+
+            # Status box
+            cv2.rectangle(image, (0, 0), (300, 40), (245, 117, 16), -1)
+
+            # Display class
+            cv2.putText(
+                image,
+                "COUNT",
+                (10, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.3,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                f'{str(self.counter)}, {predicted_class.split(" ")[0]}, {str(prediction_probability)}',
+                (5, 25),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Display Feet and Shoulder width ratio
+            cv2.putText(
+                image,
+                "FEET",
+                (130, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.3,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                feet_placement,
+                (125, 25),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+            # Display knee and Shoulder width ratio
+            cv2.putText(
+                image,
+                "KNEE",
+                (225, 12),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.3,
+                (0, 0, 0),
+                1,
+                cv2.LINE_AA,
+            )
+            cv2.putText(
+                image,
+                knee_placement,
+                (220, 25),
+                cv2.FONT_HERSHEY_COMPLEX,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+
+        except Exception as e:
+            print(f"Error while detecting squat errors: {e}")
diff --git a/web/server/detection/utils.py b/web/server/detection/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bffdf83a93609ab11dfad2b1661384708e35da44
--- /dev/null
+++ b/web/server/detection/utils.py
@@ -0,0 +1,147 @@
+import mediapipe as mp
+import cv2
+import numpy as np
+import datetime
+import os
+import math
+from django.conf import settings
+
+# Drawing helpers
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+# * Mediapipe Utils Functions
+def calculate_angle(point1: list, point2: list, point3: list) -> float:
+    """Calculate the angle between 3 points
+
+    Args:
+        point1 (list): Point 1 coordinate
+        point2 (list): Point 2 coordinate
+        point3 (list): Point 3 coordinate
+
+    Returns:
+        float: angle in degree
+    """
+    point1 = np.array(point1)
+    point2 = np.array(point2)
+    point3 = np.array(point3)
+
+    # Calculate algo
+    angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(
+        point1[1] - point2[1], point1[0] - point2[0]
+    )
+    angleInDeg = np.abs(angleInRad * 180.0 / np.pi)
+
+    angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg
+    return angleInDeg
+
+
+def calculate_distance(pointX: list, pointY: list) -> float:
+    """Calculate distance between 2 points in a frame
+
+    Args:
+        pointX (list): First point coordinate
+        pointY (list): Second point coordinate
+
+    Returns:
+        float: _description_
+    """
+
+    x1, y1 = pointX
+    x2, y2 = pointY
+
+    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+
+
+def extract_important_keypoints(results, important_landmarks: list) -> list:
+    """Extract important landmarks' data from MediaPipe output
+
+    Args:
+        results : MediaPipe Pose output
+        important_landmarks (list): list of important landmarks
+
+    Returns:
+        list: list of important landmarks' data from MediaPipe output
+    """
+    landmarks = results.pose_landmarks.landmark
+
+    data = []
+    for lm in important_landmarks:
+        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]
+        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])
+
+    return np.array(data).flatten().tolist()
+
+
+def get_drawing_color(error: bool) -> tuple:
+    """Get drawing color for MediaPipe Pose
+
+    Args:
+        error (bool): True if correct pose, False if incorrect pose
+
+    Returns:
+        tuple: RGB colors
+    """
+    LIGHT_BLUE = (244, 117, 66)
+    LIGHT_PINK = (245, 66, 230)
+
+    LIGHT_RED = (29, 62, 199)
+    LIGHT_YELLOW = (1, 143, 241)
+
+    return (LIGHT_YELLOW, LIGHT_RED) if error else (LIGHT_BLUE, LIGHT_PINK)
+
+
+# * OpenCV util functions
+def rescale_frame(frame, percent=50):
+    """Rescale a frame from OpenCV to a certain percentage compare to its original frame
+
+    Args:
+        frame: OpenCV frame
+        percent (int, optional): percent to resize an old frame. Defaults to 50.
+
+    Returns:
+        _type_: OpenCV frame
+    """
+    width = int(frame.shape[1] * percent / 100)
+    height = int(frame.shape[0] * percent / 100)
+    dim = (width, height)
+    return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
+
+
+def save_frame_as_image(frame, message: str = None):
+    """
+    Save a frame as image to display the error
+    """
+    now = datetime.datetime.now()
+
+    if message:
+        cv2.putText(
+            frame,
+            message,
+            (50, 150),
+            cv2.FONT_HERSHEY_COMPLEX,
+            0.4,
+            (0, 0, 0),
+            1,
+            cv2.LINE_AA,
+        )
+
+    print("Saving ...")
+    cv2.imwrite(f"../data/logs/bicep_{now}.jpg", frame)
+
+
+# * Other util functions
+def get_static_file_url(file_name: str) -> str:
+    """Return static url of a file
+
+    Args:
+        file_name (str)
+
+    Returns:
+        str: Full absolute path of the file. Return None if file is not found
+    """
+
+    path = f"{settings.STATICFILES_DIRS[0]}/{file_name}"
+    print(path)
+
+    return path if os.path.exists(path) else None
diff --git a/web/server/exercise_correction/__init__.py b/web/server/exercise_correction/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/exercise_correction/asgi.py b/web/server/exercise_correction/asgi.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbbd0d3e18c64fcf8b65bc5ba5d9ed601ac8f064
--- /dev/null
+++ b/web/server/exercise_correction/asgi.py
@@ -0,0 +1,16 @@
+"""
+ASGI config for exercise_correction project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "exercise_correction.settings")
+
+application = get_asgi_application()
diff --git a/web/server/exercise_correction/settings.py b/web/server/exercise_correction/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6544ec37c68c92c6c86b5e15b20a08d5ee77ec2
--- /dev/null
+++ b/web/server/exercise_correction/settings.py
@@ -0,0 +1,133 @@
+from pathlib import Path
+import os
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = "django-insecure-rrkht2+%o9)t!upnyb@t7$bcz%x2_4j*1upb=6&@n_*#1ru847"
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ["*"]
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    "django.contrib.admin",
+    "django.contrib.auth",
+    "django.contrib.contenttypes",
+    "django.contrib.sessions",
+    "django.contrib.messages",
+    "django.contrib.staticfiles",
+    # Install libraries
+    "rest_framework",
+    "corsheaders",
+    "django_extensions",
+    # My Apps
+    "api.apps.ApiConfig",
+    "stream_video.apps.StreamVideoConfig",
+]
+
+MIDDLEWARE = [
+    "django.middleware.security.SecurityMiddleware",
+    "django.contrib.sessions.middleware.SessionMiddleware",
+    "corsheaders.middleware.CorsMiddleware",
+    "django.middleware.common.CommonMiddleware",
+    "django.middleware.csrf.CsrfViewMiddleware",
+    "django.contrib.auth.middleware.AuthenticationMiddleware",
+    "django.contrib.messages.middleware.MessageMiddleware",
+    "django.middleware.clickjacking.XFrameOptionsMiddleware",
+]
+
+CORS_ALLOW_ALL_ORIGINS = True
+
+ROOT_URLCONF = "exercise_correction.urls"
+
+TEMPLATES = [
+    {
+        "BACKEND": "django.template.backends.django.DjangoTemplates",
+        "DIRS": [
+            os.path.join(BASE_DIR, "templates"),
+        ],
+        "APP_DIRS": True,
+        "OPTIONS": {
+            "context_processors": [
+                "django.template.context_processors.debug",
+                "django.template.context_processors.request",
+                "django.contrib.auth.context_processors.auth",
+                "django.contrib.messages.context_processors.messages",
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = "exercise_correction.wsgi.application"
+
+
+# Database
+# https://docs.djangoproject.com/en/4.1/ref/settings/#databases
+
+DATABASES = {
+    "default": {
+        "ENGINE": "django.db.backends.sqlite3",
+        "NAME": BASE_DIR / "db.sqlite3",
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/4.1/topics/i18n/
+
+LANGUAGE_CODE = "en-us"
+
+TIME_ZONE = "UTC"
+
+USE_I18N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/4.1/howto/static-files/
+# STATIC_ROOT = os.path.join(BASE_DIR, "static")
+
+STATIC_URL = "/static/"
+
+STATICFILES_DIRS = [
+    os.path.join(BASE_DIR, "static"),
+]
+
+MEDIA_URL = "/media/"
+
+MEDIA_ROOT = os.path.join(BASE_DIR, "static/media")
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
diff --git a/web/server/exercise_correction/urls.py b/web/server/exercise_correction/urls.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b35f0534d6d43183f1480613e1cbdd1eb2075cb
--- /dev/null
+++ b/web/server/exercise_correction/urls.py
@@ -0,0 +1,16 @@
+from django.contrib import admin
+from django.urls import path, include, re_path
+from django.conf import settings
+from django.conf.urls.static import static
+from django.views.generic import TemplateView
+
+urlpatterns = [
+    path("admin/", admin.site.urls),
+    path("api/", include("api.urls")),
+] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
+
+# Match all other routes for Frontend to handle
+urlpatterns += [
+    path("", TemplateView.as_view(template_name="index.html"), {"resource": ""}),
+    path("<path:resource>", TemplateView.as_view(template_name="index.html")),
+]
diff --git a/web/server/exercise_correction/wsgi.py b/web/server/exercise_correction/wsgi.py
new file mode 100644
index 0000000000000000000000000000000000000000..e48b3b07c373cc319f48c6728488242d8a601d80
--- /dev/null
+++ b/web/server/exercise_correction/wsgi.py
@@ -0,0 +1,16 @@
+"""
+WSGI config for exercise_correction project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "exercise_correction.settings")
+
+application = get_wsgi_application()
diff --git a/web/server/manage.py b/web/server/manage.py
new file mode 100644
index 0000000000000000000000000000000000000000..67a579536cac9691500997504a4f37a903f3a624
--- /dev/null
+++ b/web/server/manage.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "exercise_correction.settings")
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/web/server/scripts/__init__.py b/web/server/scripts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/scripts/static_remover.py b/web/server/scripts/static_remover.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec882ab7d3c17ca0d1519dbf7095870f6238348
--- /dev/null
+++ b/web/server/scripts/static_remover.py
@@ -0,0 +1,20 @@
+import os
+from detection.utils import get_static_file_url
+
+
+def run(*args, **kwargs):
+    image_folder_path = get_static_file_url("images")
+    image_files = os.listdir(image_folder_path)
+    print(f"Removing {len(image_files)} images ...")
+    for file in image_files:
+        file_path = os.path.join(image_folder_path, file)
+        os.remove(file_path)
+    print("Finished clear the image folder.")
+
+    video_folder_path = get_static_file_url("media")
+    video_files = os.listdir(video_folder_path)
+    print(f"Removing {len(video_files)} videos ...")
+    for file in video_files:
+        file_path = os.path.join(video_folder_path, file)
+        os.remove(file_path)
+    print("Finished clear the video folder.")
diff --git a/web/server/static/model/bicep_curl_input_scaler.pkl b/web/server/static/model/bicep_curl_input_scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..49df696dfd01be41206f2ce079fcd0be0ca11dbe
--- /dev/null
+++ b/web/server/static/model/bicep_curl_input_scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c07942166c3fa393e9fee1fed9228a963fd8cb81111d25b2d6d77aa90d4a4e2
+size 1949
diff --git a/web/server/static/model/bicep_curl_model.pkl b/web/server/static/model/bicep_curl_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..7edd4fa5fe17e0be016e9b3f574a217883d79bf2
--- /dev/null
+++ b/web/server/static/model/bicep_curl_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6db6bcacdc92f1af9d455e86936ac0437f4b4c3c352c169e790e3d0bb66454e4
+size 3640626
diff --git a/web/server/static/model/lunge_err_model.pkl b/web/server/static/model/lunge_err_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..10839aa9d4a50ba7e572ca6f5b7600b117700638
--- /dev/null
+++ b/web/server/static/model/lunge_err_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6cb42b6c2bc397de15c44eb56a63dbfea4e8e9c5e6749517742f5329ae4af76
+size 1120
diff --git a/web/server/static/model/lunge_input_scaler.pkl b/web/server/static/model/lunge_input_scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..eb2b504820e3fe07c522e64fbe348e5c996535ad
--- /dev/null
+++ b/web/server/static/model/lunge_input_scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d457c53018c4dc91702e4da9b5dcb5d9a06100e2a631666645c356b7efaf87f3
+size 2605
diff --git a/web/server/static/model/lunge_stage_model.pkl b/web/server/static/model/lunge_stage_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..ebf5be2147256e9883440b1da7df64ed032a2d79
--- /dev/null
+++ b/web/server/static/model/lunge_stage_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13ed109d45934b90013cf4843508e2551797794089b6b5eb70d1efaf3f0e0836
+size 1974
diff --git a/web/server/static/model/plank_input_scaler.pkl b/web/server/static/model/plank_input_scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..237c3907b33dac669fec42232b25b6915f5f5127
--- /dev/null
+++ b/web/server/static/model/plank_input_scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d45d71b2c452254952c397813d7f656d45751f6233acd7eef1c2a1a7646d399
+size 3237
diff --git a/web/server/static/model/plank_model.pkl b/web/server/static/model/plank_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..4f64e630149b106614253362dbe1f49f98356e7a
--- /dev/null
+++ b/web/server/static/model/plank_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6475585619029bc2eaa662504585dab4f88d93b37df87487bb07f0978eedaee3
+size 2356
diff --git a/web/server/static/model/squat_model.pkl b/web/server/static/model/squat_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5ac01abb32fcce30f85770dbc2542e7570dad511
--- /dev/null
+++ b/web/server/static/model/squat_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b674f85ff1121fd25c501836b67f9fa612520ca72d53ec1e0fe91c16f9c06988
+size 75198
diff --git a/web/server/stream_video/__init__.py b/web/server/stream_video/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/stream_video/admin.py b/web/server/stream_video/admin.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c38f3f3dad51e4585f3984282c2a4bec5349c1e
--- /dev/null
+++ b/web/server/stream_video/admin.py
@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.
diff --git a/web/server/stream_video/apps.py b/web/server/stream_video/apps.py
new file mode 100644
index 0000000000000000000000000000000000000000..d65fbdb47a90d10e39277d59a3499b2a9b6992e0
--- /dev/null
+++ b/web/server/stream_video/apps.py
@@ -0,0 +1,15 @@
+import sys
+from django.apps import AppConfig
+
+
+class StreamVideoConfig(AppConfig):
+    default_auto_field = "django.db.models.BigAutoField"
+    name = "stream_video"
+
+    def ready(self):
+        if "runserver" not in sys.argv:
+            return True
+
+        from detection.main import load_machine_learning_models
+
+        load_machine_learning_models()
diff --git a/web/server/stream_video/models.py b/web/server/stream_video/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/server/stream_video/tests.py b/web/server/stream_video/tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ce503c2dd97ba78597f6ff6e4393132753573f6
--- /dev/null
+++ b/web/server/stream_video/tests.py
@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.
diff --git a/web/server/stream_video/urls.py b/web/server/stream_video/urls.py
new file mode 100644
index 0000000000000000000000000000000000000000..bea8c5f0f8be59effbb1479560163ec22d070d4b
--- /dev/null
+++ b/web/server/stream_video/urls.py
@@ -0,0 +1,7 @@
+from django.urls import path
+from . import views
+
+urlpatterns = [
+    path("stream", views.stream_video, name="stream"),
+    path("upload", views.upload_video, name="upload"),
+]
diff --git a/web/server/stream_video/views.py b/web/server/stream_video/views.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f860c9569ffafce7689d4959369f312f14a63e0
--- /dev/null
+++ b/web/server/stream_video/views.py
@@ -0,0 +1,116 @@
+import os
+import mimetypes
+import traceback
+from datetime import datetime
+from wsgiref.util import FileWrapper
+
+from rest_framework import status
+from rest_framework.decorators import api_view, parser_classes
+from rest_framework.parsers import MultiPartParser
+from django.http import StreamingHttpResponse
+from django.http import JsonResponse
+
+from detection.main import exercise_detection
+from detection.utils import get_static_file_url
+
+
+@api_view(["GET"])
+def stream_video(request):
+    """
+    Query: video_name
+    Stream video get from query
+    """
+    video_name = request.GET.get("video_name")
+    if not video_name:
+        return JsonResponse(
+            status=status.HTTP_400_BAD_REQUEST,
+            data={
+                "message": "File name not given",
+            },
+        )
+
+    static_url = get_static_file_url(f"media/{video_name}")
+    if not static_url:
+        return JsonResponse(
+            status=status.HTTP_404_NOT_FOUND,
+            data={
+                "message": "File not found",
+            },
+        )
+
+    # Streamed video as chunked
+    video_size = os.path.getsize(static_url)
+    content_type, _ = mimetypes.guess_type(static_url)
+    content_type = content_type or "application/octet-stream"
+
+    chunk_size = video_size // 10
+
+    response = StreamingHttpResponse(
+        FileWrapper(open(static_url, "rb"), chunk_size), content_type=content_type
+    )
+    response["Content-Length"] = video_size
+    response["Accept-Ranges"] = "bytes"
+    return response
+
+
+@api_view(["POST"])
+@parser_classes([MultiPartParser])
+def upload_video(request):
+    exercise_type = request.GET.get("type")
+    if not exercise_type:
+        return JsonResponse(
+            status=status.HTTP_400_BAD_REQUEST,
+            data={
+                "message": "Exercise type has not given",
+            },
+        )
+
+    try:
+        if request.method == "POST":
+            video = request.FILES["file"]
+
+            # Convert any video to .mp4
+            now = datetime.now()
+            now = int(now.strftime("%Y%m%d%H%M%S"))
+            name_to_save = f"video_{now}.mp4"
+
+            # Process and Saved Video
+            results, *other_data = exercise_detection(
+                video_file_path=video.temporary_file_path(),
+                video_name_to_save=name_to_save,
+                exercise_type=exercise_type,
+                rescale_percent=40,
+            )
+
+            # Convert images' path to URL
+            host = request.build_absolute_uri("/")
+            for index, error in enumerate(results):
+                if error["frame"]:
+                    results[index]["frame"] = host + f"static/images/{error['frame']}"
+
+            response_data = {
+                "type": exercise_type,
+                "processed": True,
+                "file_name": name_to_save,
+                "details": results,
+            }
+
+            # Handle others data
+            if exercise_type in ["squat", "lunge", "bicep_curl"]:
+                response_data["counter"] = other_data[0]
+
+            return JsonResponse(
+                status=status.HTTP_200_OK,
+                data=response_data,
+            )
+
+    except Exception as e:
+        print(f"Error Video Processing: {e}")
+        # traceback.print_exc()
+
+        return JsonResponse(
+            status=status.HTTP_400_BAD_REQUEST,
+            data={
+                "error": f"Error: {e}",
+            },
+        )
diff --git a/web/server/templates/index.html b/web/server/templates/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..483e822f21c8f6848e2055b14be304e6b11c1284
--- /dev/null
+++ b/web/server/templates/index.html
@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Exercise Correction</title>
+    </head>
+    <body>
+        <h1>This is a placeholder file for Frontend Build</h1>
+    </body>
+</html>