{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# <b> <span style='color:#0386f7de'>|</span> Abstract</b>\n",
        "\n",
        "### The objective of this Notebook is to predict `HeartDisease` using all the Personal Key Indicators of the Human Beings with HeartDisease information by performing Data Exploration, Data Cleaning, Data preprocessing, Feature Engineering, Model Building, Interpreting the best model using SHAP and Build a Heart Disease Application using the best model we develop.\n",
        "\n",
        "* Dataset: Personal key Indicators of HeartDisease.\n",
        "* Dataset has 319795 observations and 18 features [14 Categorical features and 4 Continuous Features] with just 27,373 (8.5%) HeartDisease (Target) observations.\n",
        "\n",
        "Categorical Features:  \n",
        "`HeartDisease`, `Smoking`, `AlcoholDrinking`, `Stroke`, `DiffWalking`, `Sex`, `AgeCategory`, `Race`, `Diabetic`, `PhysicalActivity`, `GenHealth`, `Asthma`, `KidneyDisease`, `SkinCancer`\n",
        "\n",
        "Continuous Features:  \n",
        "`BMI`, `PhysicalHealtH`, `MentalHealth`,  `SleepTime`\n",
        "\n"
      ],
      "metadata": {
        "id": "49oaZTnJcCiK"
      },
      "id": "49oaZTnJcCiK"
    },
    {
      "cell_type": "markdown",
      "id": "cfad766f",
      "metadata": {
        "id": "cfad766f"
      },
      "source": [
        "# <b>1 <span style='color:#0386f7de'>|</span> Importing libraries</b>\n",
        "- **For ML Models**: sklearn \n",
        "- **For Data Manipulation**: numpy, pandas, sklearn\n",
        "- **For Data Visualization**: matplotlib, seaborn, plotly"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install category_encoders"
      ],
      "metadata": {
        "id": "47OTvliqdUtO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d177ef6-d6cf-4758-80ef-f2efe8e29885"
      },
      "id": "47OTvliqdUtO",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting category_encoders\n",
            "  Downloading category_encoders-2.6.0-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.2/81.2 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (1.2.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (0.13.5)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (1.22.4)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (0.5.3)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from category_encoders) (1.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.0.5->category_encoders) (2022.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.2.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.9/dist-packages (from statsmodels>=0.9.0->category_encoders) (23.1)\n",
            "Installing collected packages: category_encoders\n",
            "Successfully installed category_encoders-2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "135743bd",
      "metadata": {
        "id": "135743bd"
      },
      "outputs": [],
      "source": [
        "# Importing Required Libraries\n",
        "# Install Numpy, Pandas, Matplotlib, Seaborn, Plotly, Category_encoders\n",
        "\n",
        "# For Data Manipulation\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas.api.types import CategoricalDtype\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "\n",
        "# For Data Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "# For Data Preprocessing\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import category_encoders as ce\n",
        "from sklearn.model_selection import train_test_split \n",
        "\n",
        "# For ML models\n",
        "from sklearn.metrics import r2_score, mean_squared_error \n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.linear_model import LinearRegression ,LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC ,SVR\n",
        "from sklearn.metrics import *\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1bb16f9",
      "metadata": {
        "id": "b1bb16f9"
      },
      "source": [
        "# <b>2 <span style='color:#0386f7de'>|</span> About the Dataset</b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1eafd8bc",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "1eafd8bc",
        "outputId": "d337776b-7759-492c-8bac-196a7a0bd59f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset has 319795 observations with 18 variables.\n",
            "Target Distribution --> \n",
            "No     91.440454\n",
            "Yes     8.559546\n",
            "Name: HeartDisease, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  HeartDisease  BMI_value Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
              "0           No      16.60     Yes              No     No             3.0   \n",
              "1           No      20.34      No              No    Yes             0.0   \n",
              "2           No      26.58     Yes              No     No            20.0   \n",
              "3           No      24.21      No              No     No             0.0   \n",
              "4           No      23.71      No              No     No            28.0   \n",
              "\n",
              "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
              "0          30.0          No  Female        55-59  White      Yes   \n",
              "1           0.0          No  Female  80 or older  White       No   \n",
              "2          30.0          No    Male        65-69  White      Yes   \n",
              "3           0.0          No  Female        75-79  White       No   \n",
              "4           0.0         Yes  Female        40-44  White       No   \n",
              "\n",
              "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
              "0              Yes  Very good        5.0    Yes            No        Yes  \n",
              "1              Yes  Very good        7.0     No            No         No  \n",
              "2              Yes       Fair        8.0    Yes            No         No  \n",
              "3               No       Good        6.0     No            No        Yes  \n",
              "4              Yes  Very good        8.0     No            No         No  "
            ],
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              "      <th></th>\n",
              "      <th>HeartDisease</th>\n",
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              "      <td>7.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No</td>\n",
              "      <td>26.58</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>24.21</td>\n",
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              "      <td>No</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>6.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Dataset Link\n",
        "data_githublink = \"https://github.com/jkkn31/KrishnakanthNaik/blob/main/UI_to_Predict_HeartDisease/Data/heart_2020_cleaned.csv\"\n",
        "\n",
        "# Transforming above link to access data from above provided github link\n",
        "data_githublink= data_githublink.replace(\"blob/\", \"\").replace(\"github.com\",\"raw.githubusercontent.com\")\n",
        "data_githublink\n",
        "\n",
        "# Importing the data from github into dataframe\n",
        "raw_df = pd.read_csv(data_githublink)\n",
        "df = raw_df.copy()\n",
        "\n",
        "df.rename(columns={'BMI':'BMI_value'}, inplace=True)\n",
        "print(f\"Dataset has {df.shape[0]} observations with {df.shape[1]} variables.\")\n",
        "print(f\"Target Distribution --> \\n{100*df.HeartDisease.value_counts(normalize=True)}\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "102eda72",
      "metadata": {
        "id": "102eda72"
      },
      "source": [
        "### Dataset has `319795` observations and `18` features [14 Categorical features and 4 Continuous Features] with just `27,373 (8.5%) HeartDisease (Target) observations`."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "870ae4ed",
      "metadata": {
        "id": "870ae4ed"
      },
      "source": [
        "\n",
        "## Column Descriptions\n",
        "- `HeartDisease`: Respondents that have ever reported having coronary heart disease (CHD) or myocardial infarction (MI).\n",
        "- `BMI`: Body Mass Index (BMI).\n",
        "- `Smoking`: Have you smoked at least 100 cigarettes in your entire life?\n",
        "- `AlcoholDrinking`: Heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week\n",
        "- `Stroke`: (Ever told) (you had) a stroke?\n",
        "- `PhysicalHealth`: Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? (0-30 days).\n",
        "- `MentalHealth`: Thinking about your mental health, for how many days during the past 30 days was your mental health not good? (0-30 days).\n",
        "- `DiffWalking`: Do you have serious difficulty walking or climbing stairs?\n",
        "- `Sex`: Are you male or female?\n",
        "- `AgeCategory`: 13-categories of age. [ '18-24',  '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']\n",
        "- `Race`: Imputed race/ethnicity value.\n",
        "- `Diabetic`: (Ever told) (you had) diabetes?\n",
        "- `PhysicalActivity`: Adults who reported doing physical activity or exercise during the past 30 days other than their regular job.\n",
        "- `GenHealth`: Would you say that in general your health is...\n",
        "- `SleepTime`: On average, how many hours of sleep do you get in a 24-hour period?\n",
        "- `Asthma`: (Ever told) (you had) asthma?\n",
        "- `KidneyDisease`: Not including kidney stones, bladder infection or incontinence, were you ever told you had kidney disease?\n",
        "- `SkinCancer`: (Ever told) (you had) skin cancer?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8a405d85",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a405d85",
        "outputId": "6a91ee15-aee4-4cf6-ece2-9ee91743ca28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 319795 entries, 0 to 319794\n",
            "Data columns (total 18 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   HeartDisease      319795 non-null  object \n",
            " 1   BMI_value         319795 non-null  float64\n",
            " 2   Smoking           319795 non-null  object \n",
            " 3   AlcoholDrinking   319795 non-null  object \n",
            " 4   Stroke            319795 non-null  object \n",
            " 5   PhysicalHealth    319795 non-null  float64\n",
            " 6   MentalHealth      319795 non-null  float64\n",
            " 7   DiffWalking       319795 non-null  object \n",
            " 8   Sex               319795 non-null  object \n",
            " 9   AgeCategory       319795 non-null  object \n",
            " 10  Race              319795 non-null  object \n",
            " 11  Diabetic          319795 non-null  object \n",
            " 12  PhysicalActivity  319795 non-null  object \n",
            " 13  GenHealth         319795 non-null  object \n",
            " 14  SleepTime         319795 non-null  float64\n",
            " 15  Asthma            319795 non-null  object \n",
            " 16  KidneyDisease     319795 non-null  object \n",
            " 17  SkinCancer        319795 non-null  object \n",
            "dtypes: float64(4), object(14)\n",
            "memory usage: 43.9+ MB\n"
          ]
        }
      ],
      "source": [
        "# checking the details (missing values, dtypes) of all the variables.\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f545789b",
      "metadata": {
        "id": "f545789b"
      },
      "source": [
        "#### Dataset has `18` features [14 Categorical features and 4 Continuous Features] with just `27,373 (8.5%) HeartDisease (Target) observations`.\n",
        "\n",
        "Categorical Features:  \n",
        "`HeartDisease`, `Smoking`, `AlcoholDrinking`, `Stroke`, `DiffWalking`, `Sex`, `AgeCategory`, `Race`, `Diabetic`, `PhysicalActivity`, `GenHealth`, `Asthma`, `KidneyDisease`, `SkinCancer`\n",
        "\n",
        "Continuous Features:  \n",
        "`BMI`, `PhysicalHealtH`, `MentalHealth`,  `SleepTime`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "f0467b2e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0467b2e",
        "outputId": "e2c0d34e-0830-48e2-8477-3373dcdc559c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HeartDisease        0\n",
              "BMI_value           0\n",
              "Smoking             0\n",
              "AlcoholDrinking     0\n",
              "Stroke              0\n",
              "PhysicalHealth      0\n",
              "MentalHealth        0\n",
              "DiffWalking         0\n",
              "Sex                 0\n",
              "AgeCategory         0\n",
              "Race                0\n",
              "Diabetic            0\n",
              "PhysicalActivity    0\n",
              "GenHealth           0\n",
              "SleepTime           0\n",
              "Asthma              0\n",
              "KidneyDisease       0\n",
              "SkinCancer          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Checking Missing/null values in the dataset.\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c159b2cf",
      "metadata": {
        "id": "c159b2cf"
      },
      "source": [
        "\n",
        "* There are `no missing values` in the dataset, so Imputation methods are not required."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37ce6dc6",
      "metadata": {
        "id": "37ce6dc6"
      },
      "source": [
        "# <b>3 <span style='color:#0386f7de'>|</span> Univariate Analysis</b>\n",
        "\n",
        "Categorical Features:  \n",
        "`HeartDisease`, `Smoking`, `AlcoholDrinking`, `Stroke`, `DiffWalking`, `Sex`, `AgeCategory`, `Race`, `Diabetic`, `PhysicalActivity`, `GenHealth`, `Asthma`, `KidneyDisease`, `SkinCancer`\n",
        "\n",
        "Continuous Features:  \n",
        "`BMI`, `PhysicalHealtH`, `MentalHealth`,  `SleepTime`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be7690db",
      "metadata": {
        "id": "be7690db"
      },
      "source": [
        "## <b>3.1 <span style='color:#0386f7de'>|</span> Continuous Feature Analysis:</b>\n",
        "\n",
        "Continuous Features:  \n",
        "`BMI`, `PhysicalHealtH`, `MentalHealth`,  `SleepTime`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "77f31134",
      "metadata": {
        "id": "77f31134"
      },
      "outputs": [],
      "source": [
        "# Listing numerical and categorical features from our dataset.\n",
        "numerical_feats = df.select_dtypes(include='number').columns.tolist()\n",
        "categorical_feats = df.select_dtypes(include='object').columns.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ce502116",
      "metadata": {
        "id": "ce502116"
      },
      "source": [
        "### Descriptive Statistics of the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7ece0e10",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7ece0e10",
        "outputId": "bfc0de54-aef2-4b66-cd11-9cbae3a9cf92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           BMI_value  PhysicalHealth   MentalHealth      SleepTime\n",
              "count  319795.000000    319795.00000  319795.000000  319795.000000\n",
              "mean       28.325399         3.37171       3.898366       7.097075\n",
              "std         6.356100         7.95085       7.955235       1.436007\n",
              "min        12.020000         0.00000       0.000000       1.000000\n",
              "25%        24.030000         0.00000       0.000000       6.000000\n",
              "50%        27.340000         0.00000       0.000000       7.000000\n",
              "75%        31.420000         2.00000       3.000000       8.000000\n",
              "max        94.850000        30.00000      30.000000      24.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01d33ba5-1d8a-45b2-9be8-ad060ff6a9a1\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BMI_value</th>\n",
              "      <th>PhysicalHealth</th>\n",
              "      <th>MentalHealth</th>\n",
              "      <th>SleepTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>319795.000000</td>\n",
              "      <td>319795.00000</td>\n",
              "      <td>319795.000000</td>\n",
              "      <td>319795.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28.325399</td>\n",
              "      <td>3.37171</td>\n",
              "      <td>3.898366</td>\n",
              "      <td>7.097075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.356100</td>\n",
              "      <td>7.95085</td>\n",
              "      <td>7.955235</td>\n",
              "      <td>1.436007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.020000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.030000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.340000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31.420000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>94.850000</td>\n",
              "      <td>30.00000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>24.000000</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9e60983",
      "metadata": {
        "id": "d9e60983"
      },
      "source": [
        "### About Numerical Features:\n",
        "1. `BMI` - Average BMI (Body Mass Index) of this dataset is 28.33 and most of the data is distributed between 12.02 to 31.42 with a maximum value of 94.85. There seems to be potentinal outlier in the dataset which we can investigate later in the notebook.\n",
        "2. `PhysicalHealth` and `MentalHealth` are following same distribution with small proportion of data in 1st and 2nd quantiles. Most of the people are not well for hardly 2 to 3 days but a portion of people are suffering from so many days.\n",
        "3. `Sleep Time [Average Sleep Time]` - As expected, the Average sleep time is around 7 hours with minimum of 1 hour and maximum of 24 hours which is shocking, definitely this is a data issue as no one can sleep for 1 hour or 24 hours on average per day."
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(data=df[numerical_feats])  # outliers are ignore to be plotted\n",
        "plt.xlabel(\"Numerical Attributes\", fontsize= 12)\n",
        "plt.ylabel(\"Values\", fontsize= 12)\n",
        "plt.title(\"Numerical Attributes Boxplot\", fontsize= 15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bec2e9d0",
      "metadata": {
        "id": "bec2e9d0"
      },
      "source": [
        "### It is very evident from above box plot that all the numerical features has outliers and as the scales are different for each feature, it is hard to visualize them here. Lets normalize them later in this notebook during data transformation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "aa92abb4",
      "metadata": {
        "id": "aa92abb4"
      },
      "outputs": [],
      "source": [
        "# defining my own functions to plot required charts as well as to identify the outliers\n",
        "\n",
        "def get_quantile_limits_for_outliers(df, feature,factor):\n",
        "    Q1 = df[f\"{feature}\"].quantile(0.25)\n",
        "    Q3 = df[f\"{feature}\"].quantile(0.75)\n",
        "    IQR=Q3-Q1\n",
        "    print(f\"{feature} - Quantile information\\n\")\n",
        "    print(\"First Quartile: \",Q1)\n",
        "    print(\"Third Quartile: \",Q3)\n",
        "    print(\"Interquartile: \",IQR)\n",
        "    lower_limit=Q1-factor*IQR\n",
        "    upper_limit=Q3+factor*IQR\n",
        "    if((lower_limit < 0) &( df[f\"{feature}\"].quantile(0)>=0)):\n",
        "        print(\"Adjusted the Limits as the lower limit is less than Zero!\")\n",
        "        upper_limit = upper_limit - lower_limit \n",
        "        lower_limit = 0\n",
        "        \n",
        "    print(\"Lower Limit: \",lower_limit)\n",
        "    print(\"Upper Limi: \",upper_limit)\n",
        "    outliers_records = df[(df.SleepTime <  lower_limit) | (df.SleepTime > upper_limit)].shape[0]\n",
        "    total_records = df.shape[0]\n",
        "    per_outliers = (100*(outliers_records/total_records))\n",
        "    print(f\"% outliers in {feature} --> {per_outliers} \")\n",
        "    \n",
        "# Defining few user defined functions to plot required charts.\n",
        "import statistics\n",
        "\n",
        "def plot_kde(feature, df_numerical):\n",
        "    figure, axis = plt.subplots(1, 1, figsize=(15, 5))\n",
        "    sns.kdeplot(df_numerical.loc[(df_numerical['HeartDisease']=='Yes'), feature], color='g', shade=True, label='Yes')\n",
        "    sns.kdeplot(df_numerical.loc[(df_numerical['HeartDisease']=='No'), feature],  shade=True, label='No')\n",
        "    plt.title(f'{feature} with Heart Disease')\n",
        "    plt.legend(title='HeartDisease?', fontsize=15, title_fontsize=15)\n",
        "    \n",
        "    plt.show()\n",
        "\n",
        "def plot_hist(feature):\n",
        "    fig, ax = plt.subplots(2, 1, figsize=(15, 8))\n",
        "    sns.histplot(data = df[feature], kde = True, ax = ax[0],color='black')\n",
        "    ax[0].axvline(x = df[feature].mean(), color = 'blue', linestyle = '--', linewidth = 2, label = 'Mean: {}'.format(round(df[feature].mean(), 3)))\n",
        "    ax[0].axvline(x = df[feature].median(), color = 'red', linewidth = 2, label = 'Median: {}'.format(round(df[feature].median(), 3)))\n",
        "    ax[0].axvline(x = statistics.mode(df[feature]), color = 'green', linewidth = 2, label = 'Mode: {}'.format(statistics.mode(df[feature])))\n",
        "    ax[0].legend()\n",
        "    \n",
        "    sns.boxplot(x = df[feature], ax = ax[1],color='yellow')\n",
        "    \n",
        "    plt.show()\n",
        "    \n",
        "def plot_box_num(feature):\n",
        "    fig = plt.subplots(figsize=(14, 4))\n",
        "    sns.boxplot(x = df[feature],color='orange')\n",
        "    plt.show()\n",
        "    \n",
        "# Defining few functions to plot distributions for categorical feature\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "def plot_cat_dist_with_target(df, feature):\n",
        "    df['Target'] = df.HeartDisease.map({'Yes':1, 'No':0})\n",
        "    x = df.pivot_table(index=f'{feature}', columns='HeartDisease', values='Target', aggfunc=['count'])\n",
        "    x.columns = ['_'.join(col) for col in x.columns.values]\n",
        "    x = x.reset_index()\n",
        "    x['total'] = x[['count_No', 'count_Yes']].sum(axis=1)\n",
        "    x[\"% HeartDisease\"] = (100*x['count_Yes']/x['total']).round(2)\n",
        "    x[\"% DataDist\"] = (100*x['total']/x['total'].sum()).round(2)\n",
        "    df = x.sort_values(f\"{feature}\", ascending=True).reset_index(drop=True)\n",
        "    \n",
        "    fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
        "\n",
        "    fig.add_trace(\n",
        "        go.Scatter(x=df[f'{feature}'], y=df[\"% HeartDisease\"], name=\"% Suffered with Heart Disease\", mode=\"lines\"),\n",
        "        secondary_y=True\n",
        "    )\n",
        "\n",
        "    fig.add_trace(\n",
        "        go.Bar(x=df[f'{feature}'], y= df[\"% DataDist\"], name=f\"% Frequency of {feature}\"),\n",
        "        secondary_y=False\n",
        "    )\n",
        "\n",
        "    fig.update_xaxes(title_text=f\"{feature}\")\n",
        "\n",
        "    # Set y-axes titles\n",
        "    fig.update_yaxes(title_text=f\"% Frequency of {feature}\", secondary_y=False)\n",
        "    fig.update_yaxes(title_text=\"% Suffered with Heart Disease\", secondary_y=True)\n",
        "#     fig['layout'].update(height=300, width=20000, title='Subplots with Shared X-Axes')\n",
        "    fig.update_layout(height=450, width=950, title_text=f\"{feature} - Distribution with HeartDisease Trace\")\n",
        "    fig.show()\n",
        "    df.head()\n",
        "\n",
        "def plot_kde(feature, df_categorical):\n",
        "    plt.subplots(1, 1, figsize=(15, 5))\n",
        "    sns.histplot(data=df_categorical, x=feature, hue=\"HeartDisease\", multiple=\"dodge\", shrink=.8, hue_order = ['Yes', 'No'])\n",
        "    plt.title(f'{feature} with Heart Disease')\n",
        "    plt.show()\n",
        "    \n",
        "def plot_cat_feat(feat, df):\n",
        "    ax = plt.figure(figsize=(18,6))\n",
        "    ax = plt.subplot(1,2,1)\n",
        "    ax = sns.countplot(x=f'{feat}', data=df)\n",
        "    ax.bar_label(ax.containers[0])\n",
        "    plt.title(f\"{feat}\", fontsize=20,color='Black',font='Times New Roman')\n",
        "    ax =plt.subplot(1,2,2)\n",
        "    ax=df[f'{feat}'].value_counts().plot.pie(autopct='%1.2f%%');\n",
        "    ax.set_title(label = f\"{feat}\", fontsize = 20,color='Black',font='Times New Roman');\n",
        "    \n",
        "    \n",
        "# for i in range(1, len(binary_cols)):\n",
        "def plot_pie_for_binary_categorical(df, feature):\n",
        "    fig = plt.figure(figsize=(18,6), dpi=90)\n",
        "    \n",
        "    # Plot distribution of adults with heart disease\n",
        "    ax1 = plt.subplot(1,2,1)\n",
        "    df[df['HeartDisease'] == 'Yes'].groupby(df[binary_cols[i]]).HeartDisease.count().plot(kind='pie', autopct='%.1f%%', labeldistance=None,\n",
        "                                                                                        wedgeprops = { 'linewidth' : 1.5, 'edgecolor' : 'white', 'width':1.0 })\n",
        "    plt.gca().axes.get_yaxis().set_visible(False)\n",
        "    plt.title(\"With heart disease\")\n",
        "    \n",
        "    # Plot distribution of adults without heart disease\n",
        "    ax2 = plt.subplot(1,2,2)\n",
        "    df[df['HeartDisease'] == 'No'].groupby(df[binary_cols[i]]).HeartDisease.count().plot(kind='pie',   autopct='%.1f%%', labeldistance=None,\n",
        "                                                                                     wedgeprops = { 'linewidth' : 1.5, 'edgecolor' : 'white', 'width':1.0 })\n",
        "    plt.gca().axes.get_yaxis().set_visible(False)\n",
        "    plt.title(\"Without heart disease\")\n",
        "    plt.suptitle(f\"{binary_cols[i]} - Patient with/without Heart Disease distribution by \" + binary_cols[i] + \" status\", fontweight='bold')\n",
        "    \n",
        "    handles, labels = ax1.get_legend_handles_labels()\n",
        "\n",
        "        \n",
        "    leg = fig.legend(handles, labels, loc = 'upper right', fancybox=False)\n",
        "\n",
        "        \n",
        "    plt.subplots_adjust(right=0.9)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7002d2e6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7002d2e6",
        "outputId": "5ffa3649-4fa6-486e-f8a4-9c49c6facf7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BMI_value - Quantile information\n",
            "\n",
            "First Quartile:  24.03\n",
            "Third Quartile:  31.42\n",
            "Interquartile:  7.390000000000001\n",
            "Lower Limit:  12.945\n",
            "Upper Limi:  42.505\n",
            "% outliers in BMI_value --> 99.68761237667881 \n"
          ]
        }
      ],
      "source": [
        "get_quantile_limits_for_outliers(df, 'BMI_value', 1.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "d65179d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "d65179d7",
        "outputId": "58019fc0-fb37-433d-8c58-d96a91cdf568"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x240 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature = 'BMI_value'\n",
        "plot_hist(feature)\n",
        "plt.figure(figsize=(15,3),dpi=80)\n",
        "sns.boxplot(x= df[feature], y=df['HeartDisease'], data=df, orient=\"h\")\n",
        "plt.title(f\"{feature} Distribution\", fontweight='bold')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd79ac72",
      "metadata": {
        "id": "dd79ac72"
      },
      "source": [
        "## BMI:\n",
        "* As expected, BMI has very large outliers and most of the data is distributed between 10 to 40. \n",
        "* BMI follows normal distribution but skewed towards right as it has outliers and it is positively skewed as the Mean (28.32) > Median (27.34)\n",
        "* While the boxplots show there are no significant differences between adults with and without heart disease in BMI. so, BMI is not making sense as even though the BMI is very extreme, few people are not diagonosed with Heart Disease. Lets create BMI class and verify it in later in this notebook.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "e7535187",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "e7535187",
        "outputId": "4a999027-d7fe-46f0-b705-e695b5504455"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x240 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature = 'PhysicalHealth'\n",
        "plot_hist(feature)\n",
        "plt.figure(figsize=(15,3),dpi=80)\n",
        "sns.boxplot(x= df[feature], y=df['HeartDisease'], data=df, orient=\"h\")\n",
        "plt.title(f\"{feature} Distribution\", fontweight='bold')\n",
        "plt.show()\n",
        "\n",
        "# plot_kde(feature, df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52c16e1e",
      "metadata": {
        "id": "52c16e1e"
      },
      "source": [
        "## PhysicalHealth:\n",
        "* PhysicalHealth also has very large outliers and most of the data (75%) is distributed between 0 to 2 days and it is really to visualize the complete data, so lets handle outliers and normalize this later in this notebook.\n",
        "* While the boxplots show there are ``significant`` differences between adults with and without heart disease in PhysicalHealth. \n",
        "* People who are physically not healthy/well for long days has high probability of getting HeartDisease.\n",
        "* The distinct distribution in physical health between these evaluated groups, further correlation analysis will be conducted later to evaluate the relationship of heart disease and PhysicalHealth."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "7df4b906",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "7df4b906",
        "outputId": "78cf7a7d-2242-447c-c43b-39a48a4fd3f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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3bpJk7AP41ltv6eGHH9ZVV11VYR5eXl5q1qxZuVWnUumS68qqdU2//Px8JSYmKjs7W4sXL3ZYMlvWUT13/7zU1FSZzWYFBQUZcVu2bCk35TEtLU033HCDJMnPz0+NGjUq1zlNS0uT3W43xi97TEtL04033ujwno0bNzYKLyQkRHv37nUYy263Ky0trdzmjlVlMplc6tbYlWU2m/mXBipU9mtrMnnUytr39fWtlXkBlUUNw5VRv3Bl1C9cGfVbnoeHhzw8POTp6VmuiTRmTOlP5Tieu3x5Zc9zXOJZt6505Ej545Xh4eFhNPU8PT3Vq1cvvfvuu4qOjlbDhg1L381kMmKk0olXXbp00YMPPlhuvKuuukqenp7avHmzcnNzNXv2bGN7taKiImVmZjqMde7Ykox/LrvG1VG2hNZkMqlVq1Z67733lJmZ6TCR7Gxl/aazexWenp4KCwvT4cOHjTyuuuoqvfbaaxo/frxOnTqloKAgoxkXGRkpT09Po9900003OeTfqlUrFRYW6tSpU+W2nCt7Pw8PD/n6+lbY7Kzs0l6plt3Io6ioSCNGjFBqaqoWLFhgFFaZoKAgNW/eXKtXr3Y4npKSoujoaIcubGZmprZs2WLEpKWladeuXercubNxrHPnzlq/fr3DppApKSmyWq2KjIyUJLVt21b+/v5atWqVEWOz2bR27dpyY/38888OU563bNmijIwMdenS5RKuCgAAAAAAwJXTv39/devW7YLbrUVHR2vfvn1q2bKlWrdu7fBTNoEqPz9fJpPJmEkoSatWrTKWE19J//3vf+Xv76969eqdN6Zx48bltiErLi7Wzz//rCZNmpSLDwwMVFhYmPz8/LRo0SK1b9/e+Oxl8T/99JPDOTt37pTJZFLjxo1r4mNdUK2a6Tdp0iR9+eWXGjt2rHJycozbHEtSy5YtZbFYNGzYMI0ZM0bNmjVTVFSUUlJStGPHDn3wwQdGbGRkpGJjYzVu3Dg9++yz8vb21owZMxQWFqaePXsacQkJCVq+fLlGjx6tAQMGaO/evUpOTtbIkSONBqK3t7cSExOVlJSkwMBA3XDDDfroo4+UkZGhhIQEY6xevXpp7ty5GjZsmEaNGqW8vDy98sor6tq1qyIiIi7/xQMAAAAAAKgBERERevPNNy8YM3z4cN17771KSEjQfffdp2uuuUa//fabtm7dqvbt26tv377q2LGjJOm5557TAw88oH379untt9+ucIZbZYwbN07//ve/tWvXrvPG/Pzzz5o+fbp69+6tRo0aKTMzU5s3b9bHH3+sUaNGOTQgW7ZsqbvuuktTpkyRVNrs/Pjjj/X000/roYcekqenpxYvXqxDhw5p8uTJxnkbNmzQL7/8ohYtWigzM1PLly/XN998o48++siIadWqlVq1aqUXX3xRp0+fVrNmzbRjxw7NmzdP99xzzxVZ+Virmn5lt4SeNm1audfWr1+vpk2bqm/fvsrLy9P8+fM1b948BQcHa/bs2cbMvDIzZ87U1KlTNWHCBBUVFSk2Nlbjx493+MO97rrrlJycrGnTpumJJ55QYGCghg8frsGDBzuMNWTIENntdi1cuFDp6ekKDw9XcnKysZxYKl2qumDBAk2ePNkoori4OI0bN64mLxEAAAAAAIDTXXfddVq6dKlmzpypSZMmKTc3V/Xr19ctt9yisLAwSaU305g6dapmz56txMREhYeH6/XXX9eIESOq9Z5ld0u+kGuuuUZWq1VvvvmmTp06JX9/f4WEhGj27Nm67bbbHGLL7pRbplWrVlqwYIHefPNNPffccyopKVGLFi00b9483XLLLUacl5eXPv74Yx06dEheXl7q0KGDFi9erNDQUCPG09NTc+bM0euvv665c+fq9OnTuvbaa/X4449ryJAh1fr8VWWycxeFWu3HH3+UJLVu3drJmdSMpn/x1FH/EjXONum55rM0dOhQZ6eEWqhpU+noUalJk7I9KWqH3Nxc7d69W+Hh4exnApdEDcOVUb9wZdQvXBn1e375+flKS0tTcHBwtW40gcuv7E66Pj4+1d4X8Eq7WF1VpU9Uq/b0AwAAAAAAAHDpaPoBAAAAAAAAbqZW7ekHAJL06adSYaH0/++nAwAAAAAAqoimH4Bap107Z2cAAAAAAIBrY3kvAAAAAAAA4GZo+gEAAAAAAABuhuW9AGqdFSukvDzJ11fq29fZ2QAAAAAA4Hpo+gGodZ58Ujp6VGrSRDpyxNnZAAAAAADgeljeCwAAAAAAALgZmn4AAAAAAAB/YElJSQoLC1OnTp1UUlJS7vUHHnhAYWFhGjt2bI283zvvvKOwsDDj+TfffKOwsDD9+OOPNTL+pVi6dKl69eql1q1b684779SXX35ZqfNOnDihYcOGKTIyUh06dNDzzz+vnJycy5zthdH0AwAAAAAA+IMzm806c+aMvv32W4fjR48e1fbt2+Xn53fZ3vumm27S4sWLFRoaetneozJWrlypF154QX369NH8+fPVpk0bDR06VNu3b7/geTabTY8//rgOHjyoV199VRMnTtTXX3+t0aNHX5nEz4M9/QAAAAAAAP7gzGazoqOjtXLlSkVFRRnHV65cqeuvv14eHpdv3pi/v7/atGlz2cavrFmzZun222/XiBEjJEkdO3bU3r179cYbb2j+/PnnPW/NmjXat2+fUlJSFBISIkmyWq1KSEjQjh07FBERcSXSL4eZfgAAAAAAAFDfvn21Zs0a2Ww249iKFSvUt2/fCuMPHDigp556Su3atVObNm30xBNP6JdffnGIycnJ0V//+ldFRkaqY8eOeuWVV1RcXOwQU9Hy3oULF+qee+5Ru3btFB0drcTERKWlpTmcN3bsWPXt21fffPON7rrrLrVp00b33nuvdu7cWeXPfvjwYR08eFB9+vRxOB4fH68tW7aosLDwvOdu3LhRYWFhRsNPkmJiYlS3bl1t2LChyrnUFJp+AAAAAAAAULdu3VRYWKhNmzZJkvbv3689e/YoPj6+XOzhw4f1wAMPKDMzU9OmTdP06dOVnp6uQYMGOTTIxo0bp88//1xjxozRyy+/rAMHDujdd9+9aC7Hjx/Xww8/rDfffFOTJ09WSUmJHnjgAWVkZDjEnTp1SpMnT1ZCQoJmzpypgoICDR061KFxOW7cOIc9BCuSmpoqSQoODnY4HhoaKpvNpsOHD1/w3LMbfpJkMpkUHBxsjOsMLO8FAAAAAACoKUuXShMmSNnZznn/OnWkv/1NuvfeKp/q6+ur7t27a+XKleratatWrFihyMhIBQUFlYudPXu2AgIC9Pbbb8vb21uS1LZtW/Xo0UNLly7VQw89pP3792vt2rWaPHmy7v3/+cTGxqpnz54XzWXcuHHGPxcXFysmJkbR0dFas2aN7r//fuO1zMxMffDBB7r++uuNz/DII4/ohx9+UGRkpCTJw8NDnp6eF3y/zMxMSaXLcs9W9rzs9YpkZWWpTp065Y4HBARc8LzLjaYfAAAAAABATfnHP6Sff3Z+DtVo+kmlS3xHjx6t/Px8paSkaODAgRXGbdq0SfHx8fL09FRRUZGk0gZZy5YtjeW1P/74o+x2u+Li4ozzPD09ddttt+mdd965YB7bt2/X66+/rl27djnM7jt48KBDXIMGDYyGnyS1aNFCUunddMtMnjxZU6dOvehndzc0/QDUOv7+pf9zyt/f2ZkAAAAAQBX99a/SCy84d6bfX/5S7dNjY2NlNpv1+uuv68iRI+X2uCtz5swZvfvuuxUu1TWbzZJKl96azWYFBAQ4vH711VdfMIdjx45p8ODBatWqlSZNmqQGDRrIbDYrMTFRBQUFDrHnzswre+9z4y6mLMfs7GzVr1/fOJ6VleXwekWsVqtycnLKHc/MzFSjRo2qlEdNoukHoNZx9v8UAwAAAIBqu/feas+yqw3MZrN69uypd955R9HR0brmmmsqjAsICFCXLl304IMPlnvtqquukiTVr19fNptNmZmZDk2z06dPXzCH//u//1Nubq5mz55tNPWKioou61LZsj35zt2fLzU1VWazucIlzmefu3fvXodjdrtdaWlpiomJuTwJVwJNPwAAAAAAABj69++v06dP67777jtvTHR0tPbt26eWLVued7+81q1bS5I+//xzY0+/4uJirVu37oLvn5+fL5PJJC+v/7WtVq1aZSwjvhyCgoLUvHlzrV69WrfddptxPCUlRdHR0bJYLOc9t3Pnzvrss8908OBBNW/eXJK0ZcsWZWRkqEuXLpct54uh6QcAAAAAAABDRESE3nzzzQvGDB8+XPfee68SEhJ033336ZprrtFvv/2mrVu3qn379urbt69atGihuLg4TZkyRQUFBWratKk+/PBDhzvrVqRjx46SpOeee04PPPCA9u3bp7fffrvcUt7KGj9+vD799FPt2rXrgnHDhg3TmDFj1KxZM0VFRSklJUU7duzQBx98YMQcPXpUcXFxevrppzV06FBJUq9evTR37lwNGzZMo0aNUl5enl555RV17dpVERER1cq5JtSqpt+hQ4eUnJysH374Qfv27VNISIhWrFhhvH7kyBH16NGjwnMtFot+/PHHC8bdfPPNWrJkicOx77//Xi+//LJ2796tq6++WgMGDNCQIUNkMpmMGLvdrvnz5+vDDz9Uenq6wsPD9dxzz6lNmzYOY504cUKTJ0/W119/LbPZrLi4OD333HPyZ2MyAAAAAADgRq677jotXbpUM2fO1KRJk5Sbm6v69evrlltuUVhYmBE3ZcoUvfTSS5o+fbosFovuvvtudejQQa+88sp5xw4LC9PUqVM1e/ZsJSYmKjw8XK+//rpGjBhRrVxLSkpUXFx80bi+ffsqLy9P8+fP17x58xQcHKzZs2cbdwGWSntExcXFstvtxjGz2awFCxZo8uTJGjVqlLy8vBQXF+dwB2JnMNnPztLJ1q1bp7/97W+6+eablZaWJrvd7tD0KywsLNeVtdvtevzxx9WxY0e98cYbkv7X9Bs1apSioqKM2Kuuusrhji6HDh3SXXfdpZiYGD300EPas2ePpk+frpEjRyohIcGImzdvnmbNmqUxY8YoLCxMixYt0ubNm/Xpp58aa7ptNpv+9Kc/SZJGjhyp/Px8vfzyy7rxxhs1d+7cal+TskZm2ZRYV9f0L5466l+ixtkmPdd8ltEVB872l79IZ85I9eqV3nSqtsjNzdXu3bsVHh4uPz8/Z6cDVBk1DFdG/cKVUb9wZdTv+eXn5ystLU3BwcHy8fFxdjqoQHFxsfLz8+Xj43PeJci1zcXqqip9olo106979+7GuumxY8cat3guY7FYys2u++abb5STk6O+ffuWG++6664rF3+25ORk1atXT6+99posFouio6OVnp6uOXPmaODAgbJYLCooKNDcuXM1ePBgDRo0SJLUrl079e7dW8nJyZo4caIkac2aNdq3b59SUlKMDR+tVqsSEhK0Y8cOp07nBFzNRx9JR49KTZrUrqYfAAAAAACuwsPZCZzNw6Pq6axYsUL+/v7q3r17lc/duHGjevTo4bAZY3x8vLKysrRt2zZJpct/c3JyHG5RbbFYFBcXp40bNzqMFRYW5nCHl5iYGNWtW1cbNmyocm4AAAAAAABAddWqmX5VZbPZtHbtWsXFxcnb27vc6xMnTtTIkSNVt25d9ejRQ2PGjFHdunUllU5R/vXXXx2adFLpbZZNJpNSU1MVFRWl1NRU4/jZQkND9e677xrTRM+9pbMkmUwmBQcHG2NUl91uV25u7iWNURvZbDbl5eWpFq0wRy1ht/tI8pDdXqLc3Hxnp2PIy8tzeARcDTUMV0b9wpVRv3Bl1O/5FRQUGHvFVWa/OFx5Zf2Gsn34XEFxcbFKSkqUl5enkpKScq/b7XaH+1BciEs3/TZu3KiMjIxyS3stFosGDBig2NhYWa1W/fDDD5ozZ4527typpUuXymw2Kzs7W5LK3fnFYrHI19dXmZmZkqSsrCxZLJZyTUWr1Sq73a7MzEz5+PgoKytLderUKZdjQECAMVZ12Ww27d69+5LGqG3sks6cOaMDBw6ooKDA2emglikqai3JoqKiolpZ+wcPHnR2CsAloYbhyqhfuDLqF66M+q2Yl5cXf6d1Aa70Z1RQUKCioqILTiA7e8Xqhbh002/58uW65pprFB0d7XC8QYMGxl57ktShQwddf/31SkxM1Oeff674+PgrnOmlMZvNatGihbPTqFEmSfXq1VNoaCgz/VCOl5eX8RgeHu7kbP4nLy9PBw8eVPPmzeXr6+vsdIAqo4bhyqhfuDLqF66M+j2/goICHTt2TN7e3tzIo5ay2+0qKCiQt7d3pWfH1QZeXl5q1qxZhata9+/fX/lxajKpK+n333/Xl19+qf79+1fqDixdunSRn5+ffvrpJ8XHxxuz8spm/JUpLCxUXl6eAgICJJXO6CssLDSKpExWVpZMJpNDXE5OTrn3zczMVKNGjar9OaXSZcLueJcks9nMvzRQobLvYpPJo1bWvq+vb63MC6gsahiujPqFK6N+4cqo3/I8PDzk4eEhT09Pl7kz7B9N2ZJek8nkMn9Gnp6e8vDwkK+vb4XN5Ko0L2vVjTyq4vPPP1d+fr7uuOOOap3v5+enRo0alZsumZaWJrvdbuzPV/aYlpbmEJeamqrGjRsbfwAhISHlxrLb7UpLSyu31x8AAAAAAABwObls02/FihVq1qyZbr755krFf/nll8rNzVXr1q2NY507d9b69etls9mMYykpKbJarYqMjJQktW3bVv7+/lq1apURU3YDkc6dOzuM9fPPPzvsc7BlyxZlZGSoS5cu1f2YAAAAAAAAQJXVquW9eXl52rBhgyTp6NGjysnJ0erVqyWV7ssXGBgoSUpPT9eWLVs0ZMiQCseZNm2aTCaT2rRpI6vVqh07dmju3Llq1aqVbrvtNiMuISFBy5cv1+jRozVgwADt3btXycnJGjlypLEpore3txITE5WUlKTAwEDdcMMN+uijj5SRkaGEhARjrF69emnu3LkaNmyYRo0apby8PL3yyivq2rWrIiIiLsv1AgAAAAAAACpSq5p+p0+f1jPPPONwrOz5e++9p6ioKEnSqlWrVFRUdN6lvaGhofroo4+0ZMkS5efnq2HDhrr33ns1fPhw4wYBknTdddcpOTlZ06ZN0xNPPKHAwEANHz5cgwcPdhhvyJAhstvtWrhwodLT0xUeHq7k5GQFBQUZMWazWQsWLNDkyZM1atQoeXl5KS4uTuPGjauRawP8kdx+u5SeLv3/Pj8AAAAAAKiiWtX0a9q0qfbs2XPRuIceekgPPfTQeV/v37+/+vfvX6n3bNu2rZYsWXLBGJPJpMTERCUmJl4wrmHDhkpKSqrU+wI4v7lznZ0BAAAAAPxxJCUlafbs2WrQoIE2bNggDw/H3eAeeOABbdu2TXfffbemTZt2ye/397//XevXr9cXX3xxyWOdzxdffKGZM2cqLS1NjRo1UmJiou65556Lnpedna2pU6dq3bp1stls6tSpk8aPH68GDRpctlwvF5fd0w8AAAAAAAA1w2w268yZM/r2228djh89elTbt293qbs3f/fddxo6dKhuvvlmJSUlqU+fPnr++eeNLeQuZMSIEdq0aZMmTpyo6dOnKy0tTUOGDFFRUdEVyLxm1aqZfgAAAAAAALjyzGazoqOjtXLlSmN7NUlauXKlrr/++nKz/2qzt956SxEREZo4caLy8/PVqVMnHTlyRLNmzVLv3r3Pe962bdv09ddfKzk5WbGxsZKk4OBgxcfHa+3atYqPj79SH6FGuM6fGAAAAAAAAC6bvn37as2aNbLZbMaxFStWqG/fvhXGf/vtt3rggQcUERGhqKgoPffcc8rIyHCIOXHihJ588kndfPPN6tSpk+bPn1/hWMePH9eYMWMUFRWliIgIPfTQQ9q5c2eVP0NhYaG++eabcs29+Ph4HThwQEeOHDnvuRs3bpTValVMTIxxLCQkROHh4dq4cWOVc3E2mn4Aap327aWmTUsfAQAAAABXRrdu3VRYWKhNmzZJkvbv3689e/ZUOMNt586deuyxx3TVVVfp9ddf15gxY/Tll19qyJAhKi4uNuKefvpp7dy5UxMnTtSLL76odevWac2aNQ5jZWZm6sEHH9TPP/+sF154QUlJSfL19dWjjz6q06dPG3FJSUkKCwu7YOPul19+kc1mU0hIiMPx0NBQSVJqaup5z01NTVVwcLBMJpPD8ZCQkAueV1uxvBdArXP8uHT0qLOzAAAAAICqW/rTUk34aoKyC7Kd8v51vOvob93+pntb3lvlc319fdW9e3etXLlSXbt21YoVKxQZGamgoKBysXPmzFH9+vU1Z84cmc1mSVKjRo2UkJCgDRs2qHv37tq4caN27typd955R9HR0ZKkqKgodenSRXXr1jXGevfdd5WVlaWlS5fq6quvliRFR0erV69eSk5O1l//+ldJpTda9fT0LNeUO1tmZqYkyWq1Ohwve172ekWysrJUp06dcscDAgKqNevQ2Wj6AQAAAAAA1JB/bP6Hfv7tZ+clkF2aQ3WaflLpEt/Ro0crPz9fKSkpGjhwYIVx3333nfr27Ws0/CQpNjZWVqtV//3vf9W9e3ft2LFDderUMRp+klSnTh3deuut2rVrl3Fs06ZNioqKUkBAgHHDDA8PD91yyy368ccfjbihQ4dq6NCh1fpcf0Q0/QAAAAAAAGrIX2P+qhe+fMGpM/3+cutfqn1+bGyszGazXn/9dR05ckR9+vSpMC4rK8uYlXe2q6++2phNd/LkSQUGBlYYc7YzZ85o+/btuummm8rFNmvWrEr5BwQESJKysx2vf1ZWlsPrFbFarTp+/Hi545mZmRc8r7ai6QcAAAAAAFBD7m15b7Vn2dUGZrNZPXv2NJbkXnPNNRXGBQQEOOy3V+b06dNGg6xBgwZKT0+vMObcsTp16qRnnnmmXKzFYqlS/s2aNZPZbFZqaqpuvfVW43jZnnzn7vV3tpCQEG3ZskV2u91hCXFaWppuuOGGKuVRG3AjDwAAAAAAABj69++vbt266ZFHHjlvTLt27bR+/XpjOa5Uukw3KytL7dq1kyS1bt1a2dnZ2rJlixGTnZ2tzZs3O4x166236sCBAwoNDVXr1q0dfsLCwqqUu8ViUVRUVLmbhaSkpCg0NFRNmzY977mdO3dWZmamQ75paWnatWuXOnfuXKU8agOafgAAAAAAADBERETozTffVNeuXc8b8+STT+rUqVNKTEzUV199pY8//lhjxoxRRESEunTpIqm0iXbTTTfpL3/5i/79739r/fr1evzxx+Xv7+8w1qBBg2QymfTwww/r3//+t7Zu3arVq1fr5Zdf1jvvvGPEzZ49Wy1bttTRi9z58amnntL27dv10ksv6bvvvlNSUpJWrFihYcOGOcS1bNlS48aNM55HRkYqNjZW48aN06pVq/TFF19o+PDhCgsLU8+ePSt59WoPlvcCAAAAAACgSlq1aqWFCxfqtdde07Bhw+Tn56fu3bvr2Weflaenp6TSu+2++eabevHFFzVhwgRZrVYNHDhQv/32m9avX2+MVa9ePS1evFgzZ87U9OnTlZGRoauvvlo333yz4uLijDi73a7i4mLZ7fYL5ta+fXslJSVp5syZWrZsmRo1aqTJkyeX25+wuLhYJSUlDsdmzpypqVOnasKECSoqKlJsbKzGjx8vLy/Xa6GZ7Be7UnCqsrvUtG7d2smZ1Iymf/HUUf8SNc426bnms7jrDirUtKl09KjUpIl05Iizs/mf3Nxc7d69W+Hh4fLz83N2OkCVUcNwZdQvXBn1C1dG/Z5ffn6+0tLSFBwcLB8fH2engwoUFxcrPz9fPj4+RiOytrtYXVWlT8TyXgAAAAAAAMDNuN7cRABu75VXpNxcif+RCAAAAABA9dD0A1DrPPigszMAAAAAAMC1sbwXAAAAAAAAcDM0/QAAAAAAAKqJ+6OiJtVkPbG8F0Cts2ePVFQkeXlJYWHOzgYAAAA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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x240 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature = 'MentalHealth'\n",
        "plot_hist(feature)\n",
        "plt.figure(figsize=(15,3),dpi=80)\n",
        "sns.boxplot(x= df[feature], y=df['HeartDisease'], data=df, orient=\"h\")\n",
        "plt.title(f\"{feature} Distribution\", fontweight='bold')\n",
        "plt.show()\n",
        "# plot_kde(feature, df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77dfe169",
      "metadata": {
        "id": "77dfe169"
      },
      "source": [
        "## MentalHealth:\n",
        "* `MentalHealth` following same distribution as `PhysicalHealth` and has outliers and most of the data (75%) is distributed between 0 to 3  days. This seems like people who are suffering from physically illness are also suffering from Mental illness. \n",
        "* While the boxplots show there are ``NO Significant`` differences between adults with and without heart disease in MentalHealth. \n",
        "* Lets handle outliers and normalize to draw further insights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "873ca989",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "873ca989",
        "outputId": "5782bb75-727f-4fd5-d1e2-5033dab4f031"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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S/gEAAABAQSn/AAAAAKCg/h/03pidzJHxdAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x240 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature = 'SleepTime'\n",
        "plot_hist(feature)\n",
        "plt.figure(figsize=(15,3),dpi=80)\n",
        "sns.boxplot(x= df[feature], y=df['HeartDisease'], data=df, orient=\"h\")\n",
        "plt.title(f\"{feature} Distribution\", fontweight='bold')\n",
        "plt.show()\n",
        "# plot_kde(feature, df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "610c9c84",
      "metadata": {
        "id": "610c9c84"
      },
      "source": [
        "## SleepTime:\n",
        "* SleepTime has outliers and most (75%) of the data is distributed between 1 to 8 hours with an average sleep time of 7.1 hours. \n",
        "* While the boxplots show there are `No Significant` differences between adults with and without heart disease in SleepTime.\n",
        "* Distribution shows minimum of 1 hour and maximum of 24 hours which is shocking, definitely this is a data issue as no one can sleep for 1 hour or 24 hours on average per day."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e15ecdac",
      "metadata": {
        "id": "e15ecdac"
      },
      "source": [
        "### What are the likely distributions of the numeric variables?\n",
        "* All the Numeric features (PhysicalHelath, MentalHealth, BMI, SleepTime) - are following Normal distributions but every feature has extreme outliers so all the distribution are SKEWED."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c58487c",
      "metadata": {
        "id": "9c58487c"
      },
      "source": [
        "### Outlier Handling: \n",
        "\n",
        "#### 1. Standard Deviation Method:\n",
        "\n",
        "* Standard deviation is a metric of variance i.e. how much the individual data points are spread out from the mean. If a dataset approximately follows normal distribution then around 68% of the data lies in 1 standard deviation from the mean, Similarly ~95% of the data lies in 2 standard deviations from the mean and ~99.7% of the data lies in 3 standard deviations from the mean.\n",
        "\n",
        "#### 2. Inter Quantile Range Method:\n",
        "* IQR is a concept in statistics that is used to measure the statistical dispersion and data variability by dividing the dataset into quartiles.\n",
        "* Q1 (1st Quantile) = df.quantile(0.25)\n",
        "* Q3 (3rd Quantile)= df.quantile(0.75)\n",
        "* IQR = Q3-Q1\n",
        "* LowerLimit = Q1-1.5*IQR\n",
        "* UpperLimit = Q3+1.5*IQR\n",
        "\n",
        "why we use 1.5 as a factor --> as Standard Devaiation method, 1 IQR from Q1 & Q2 covers around ~70% of the data and 2 IQR covers around ~ 97% of the data, so bigger scale will lead to consider outliers as a data point and a smaller scale will lead to percieve some of the datapoints has outliers. so it is aproximated that 1.5 factor will cover around ~95% of the data.\n",
        "\n",
        "Anything outside the`LowerLimit` and `UpperLimit` are can be replaced with Median or Mean or Mode."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "ee04eaa9",
      "metadata": {
        "id": "ee04eaa9"
      },
      "outputs": [],
      "source": [
        "raw_df1 = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "89ec79b4",
      "metadata": {
        "id": "89ec79b4"
      },
      "outputs": [],
      "source": [
        "df= raw_df1.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3232e6f9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "3232e6f9",
        "outputId": "140c367a-ce71-4ba4-a283-3a7df8b3a7d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  HeartDisease  BMI_value Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
              "0           No      16.60     Yes              No     No             3.0   \n",
              "1           No      20.34      No              No    Yes             0.0   \n",
              "2           No      26.58     Yes              No     No            20.0   \n",
              "3           No      24.21      No              No     No             0.0   \n",
              "4           No      23.71      No              No     No            28.0   \n",
              "\n",
              "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
              "0          30.0          No  Female        55-59  White      Yes   \n",
              "1           0.0          No  Female  80 or older  White       No   \n",
              "2          30.0          No    Male        65-69  White      Yes   \n",
              "3           0.0          No  Female        75-79  White       No   \n",
              "4           0.0         Yes  Female        40-44  White       No   \n",
              "\n",
              "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \\\n",
              "0              Yes  Very good        5.0    Yes            No        Yes   \n",
              "1              Yes  Very good        7.0     No            No         No   \n",
              "2              Yes       Fair        8.0    Yes            No         No   \n",
              "3               No       Good        6.0     No            No        Yes   \n",
              "4              Yes  Very good        8.0     No            No         No   \n",
              "\n",
              "            BMI  \n",
              "0   UnderWeight  \n",
              "1  NormalWeight  \n",
              "2    OverWeight  \n",
              "3  NormalWeight  \n",
              "4  NormalWeight  "
            ],
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              "\n",
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              "      <td>7.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NormalWeight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No</td>\n",
              "      <td>26.58</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>20.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>Fair</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>OverWeight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>24.21</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Female</td>\n",
              "      <td>75-79</td>\n",
              "      <td>White</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>No</td>\n",
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              "      <td>NormalWeight</td>\n",
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              "      <td>40-44</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NormalWeight</td>\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Lets divide BMI into Class based on below limits for each class.\n",
        "def BMI_Classification(BMI):\n",
        "    if(BMI < 18.5): return 'UnderWeight'\n",
        "    elif(18.5 <= BMI <= 25): return 'NormalWeight'\n",
        "    elif(25 <= BMI <= 30): return 'OverWeight'\n",
        "    elif(30 <= BMI <= 35): return 'Obesity Class I'\n",
        "    elif(35 <= BMI <= 40): return 'Obesity Class II'\n",
        "    elif(40 <= BMI): return 'Obesity Class III'\n",
        "    else: return None\n",
        "    \n",
        "from pandas.api.types import CategoricalDtype\n",
        "\n",
        "df['BMI'] = df['BMI_value'].apply(lambda x: BMI_Classification(x))\n",
        "\n",
        "list_ordering = [\"UnderWeight\", 'NormalWeight', 'OverWeight', 'Obesity Class I', 'Obesity Class II', 'Obesity Class III']  \n",
        "order_type = CategoricalDtype(categories=list_ordering, ordered=True)\n",
        "df[\"BMI\"] = df[\"BMI\"].astype(order_type)   \n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "838dd672",
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        "id": "838dd672",
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          },
          "metadata": {}
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      ],
      "source": [
        "plot_cat_dist_with_target(df, 'BMI')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a7648537",
      "metadata": {
        "id": "a7648537"
      },
      "source": [
        "#### Now BMI Class makes more sense and relatable than using BMI actual values, so lets drop BMI_value feature and use this class BMI\n",
        "* Except NormalWeight, all other classes are prone to HeartDisease depending on the level of class they belong to which seems more realistic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "c90d93ee",
      "metadata": {
        "id": "c90d93ee"
      },
      "outputs": [],
      "source": [
        "df = df.drop('BMI_value', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "caa5a0f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caa5a0f7",
        "outputId": "cc9e7551-0c68-44c5-a504-6cf4a1d9e5ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-29e18f3a7353>:1: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrame.quantile is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PhysicalHealth    0.0\n",
              "MentalHealth      0.0\n",
              "SleepTime         6.0\n",
              "Target            0.0\n",
              "Name: 0.25, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df.quantile(0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "62439fbf",
      "metadata": {
        "id": "62439fbf"
      },
      "outputs": [],
      "source": [
        "def remove_oultliers_using_quantiles(df, feature,factor):\n",
        "    Q1 = df[f\"{feature}\"].quantile(0.25)\n",
        "    Q3 = df[f\"{feature}\"].quantile(0.75)\n",
        "    IQR=Q3-Q1\n",
        "    lower_limit=Q1-factor*IQR\n",
        "    upper_limit=Q3+factor*IQR\n",
        "#     print(df[feature].describe())\n",
        "    df.loc[(df[feature] < lower_limit) | (df[feature] > upper_limit), feature] = df[feature].mean()\n",
        "#     print(df[feature].describe())\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "0fc0c510",
      "metadata": {
        "id": "0fc0c510"
      },
      "outputs": [],
      "source": [
        "df = remove_oultliers_using_quantiles(df, 'PhysicalHealth', 1.5)\n",
        "df = remove_oultliers_using_quantiles(df, 'SleepTime', 1.5)\n",
        "df = remove_oultliers_using_quantiles(df, 'MentalHealth', 1.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "c16e81f4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c16e81f4",
        "outputId": "b780715f-af19-4760-f808-5f3961c8e9fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['PhysicalHealth', 'MentalHealth', 'SleepTime', 'Target']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "numerical_feats = df.select_dtypes(include='number').columns.tolist()\n",
        "numerical_feats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "cecc617c",
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          "height": 498
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        "id": "cecc617c",
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(data=df[numerical_feats[:-1]])  # outliers are ignore to be plotted\n",
        "plt.xlabel(\"Numerical Attributes\", fontsize= 12)\n",
        "plt.ylabel(\"Values\", fontsize= 12)\n",
        "plt.title(\"Numerical Attributes Boxplot after removing outliers\", fontsize= 15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bdcd1a7",
      "metadata": {
        "id": "5bdcd1a7"
      },
      "source": [
        "#### After fixing outliers, lets normalize them so that all the features are in same class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "86ede2b8",
      "metadata": {
        "id": "86ede2b8"
      },
      "outputs": [],
      "source": [
        "# Using MinMaxScaler to normalize the data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df[numerical_feats[:-1]] = pd.DataFrame(scaler.fit_transform(df[numerical_feats[:-1]].values), columns=numerical_feats[:-1], index=df.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "6bd4b529",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "6bd4b529",
        "outputId": "69177784-ccd8-4602-e845-3834dfa4477b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  HeartDisease Smoking AlcoholDrinking Stroke  PhysicalHealth  MentalHealth  \\\n",
              "0           No     Yes              No     No        0.600000      0.556909   \n",
              "1           No      No              No    Yes        0.000000      0.000000   \n",
              "2           No     Yes              No     No        0.674342      0.556909   \n",
              "3           No      No              No     No        0.000000      0.000000   \n",
              "4           No      No              No     No        0.674342      0.000000   \n",
              "\n",
              "  DiffWalking     Sex  AgeCategory   Race Diabetic PhysicalActivity  \\\n",
              "0          No  Female        55-59  White      Yes              Yes   \n",
              "1          No  Female  80 or older  White       No              Yes   \n",
              "2          No    Male        65-69  White      Yes              Yes   \n",
              "3          No  Female        75-79  White       No               No   \n",
              "4         Yes  Female        40-44  White       No              Yes   \n",
              "\n",
              "   GenHealth  SleepTime Asthma KidneyDisease SkinCancer           BMI  Target  \n",
              "0  Very good      0.250    Yes            No        Yes   UnderWeight       0  \n",
              "1  Very good      0.500     No            No         No  NormalWeight       0  \n",
              "2       Fair      0.625    Yes            No         No    OverWeight       0  \n",
              "3       Good      0.375     No            No        Yes  NormalWeight       0  \n",
              "4  Very good      0.625     No            No         No  NormalWeight       0  "
            ],
            "text/html": [
              "\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Very good</td>\n",
              "      <td>0.625</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NormalWeight</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8565e595-3f2f-4b11-9abe-1fd82667d7f7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8565e595-3f2f-4b11-9abe-1fd82667d7f7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8565e595-3f2f-4b11-9abe-1fd82667d7f7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "d7eef1d3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "d7eef1d3",
        "outputId": "7e422af1-c9db-4d28-b8d0-5c6b6006eede"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(data=df[numerical_feats[:-1]])  # outliers are ignore to be plotted\n",
        "plt.xlabel(\"Numerical Attributes\", fontsize= 12)\n",
        "plt.ylabel(\"Values\", fontsize= 12)\n",
        "plt.title(\"Numerical Attributes Boxplot after removing outliers and normalized\", fontsize= 15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "683dc25d",
      "metadata": {
        "id": "683dc25d"
      },
      "source": [
        "#### Normalization: \n",
        "Normalization brings down all the features scales to [0 to 1] as it uses minimum and maximum value in each feature.\n",
        "\n",
        "Normalization = (X - Xmin) / (Xmax - Xmin).\n",
        "\n",
        "#### Standardization:\n",
        "Standardizatoin distributes the data around 1 standard devitaion from its mean of feature, so here all the features will not be in same scale as each features mean will not be same"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d3b53922",
      "metadata": {
        "id": "d3b53922"
      },
      "source": [
        "### Do the ranges of the predictor variables make sense?\n",
        "\n",
        "* It was very hard to visualize the numerical features with Raw Data when I plotted the Boxplots, as they have extreme otliers and each feature has different scale.\n",
        "* After Transforming the Data by Handling outliers and normalizing the features to bring down all the features  to same scale, the data distribution of the features makes sense except the BMI_value feature.\n",
        "* While the boxplots of BMI_value show there are no significant differences between adults with and without heart disease in BMI. so, BMI is not making sense as even though the BMI is very extreme, few people are not diagonosed with Heart Disease. so I have create a BMI class depending on the BMI values and the BMI class distribution is more realastic than BMI_values. [please refer to BMI class Distirbution chart]\n",
        "\n",
        "* Defined BMI function:\n",
        "* `def BMI_Classification(BMI):\n",
        "    if(BMI < 18.5): return 'UnderWeight'\n",
        "    elif(18.5 <= BMI <= 25): return 'NormalWeight'\n",
        "    elif(25 <= BMI <= 30): return 'OverWeight'\n",
        "    elif(30 <= BMI <= 35): return 'Obesity Class I'\n",
        "    elif(35 <= BMI <= 40): return 'Obesity Class II'\n",
        "    elif(40 <= BMI): return 'Obesity Class III'\n",
        "    else: return None`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "891c71b8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "891c71b8",
        "outputId": "a78ab081-ffde-496e-fd0c-05314dce5093"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "numerical_feats\n",
        "sns.heatmap(df[numerical_feats].corr(), annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "53ed09ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "53ed09ee",
        "outputId": "ff0ba15d-3149-4190-844f-9aa3111d1dae"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-3cb789dd811a>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumerical_feats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag_kind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'kde'\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Target\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2170\u001b[0m     \u001b[0;31m# Add a legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2172\u001b[0;31m         \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2174\u001b[0m     \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36madd_legend\u001b[0;34m(self, legend_data, title, label_order, adjust_subtitles, **kwargs)\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m             \u001b[0;31m# Draw the plot to set the bounding boxes correctly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m             \u001b[0m_draw_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_figure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0;31m# Calculate and set the new width of the figure so the legend fits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/utils.py\u001b[0m in \u001b[0;36m_draw_figure\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;34m\"\"\"Force draw of a matplotlib figure, accounting for back-compat.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;31m# See https://github.com/matplotlib/matplotlib/issues/19197 for context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 mpath.Path(offsets), offset_trf, tuple(facecolors[0]))\n\u001b[1;32m    408\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m             renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    410\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_trf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mvertices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \"\"\"\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function flush_figures at 0x7f246c1aba60> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m                 \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2367\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \"bbox_inches_restore\"}\n\u001b[1;32m   2231\u001b[0m             \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m             print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m   2233\u001b[0m                 *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m   2234\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    455\u001b[0m         *pil_kwargs* and *metadata* are forwarded).\n\u001b[1;32m    456\u001b[0m         \"\"\"\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    459\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 mpath.Path(offsets), offset_trf, tuple(facecolors[0]))\n\u001b[1;32m    408\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m             renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    410\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_trf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# sns.pairplot(df[numerical_feats], diag_kind='kde',  hue=\"Target\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e598dbbf",
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        "#### Correlation among Numerical Features: \n",
        "* Correlation matrix shows that `[PhysicalHealth]` have `positive correlation` with `Target`.\n",
        "* We can see that one of our dataset features [PhysicalHealth] have high positive association with other feature [MentalHealth] compare to other numerical features, but it is not that high. \n",
        "* It seems like there is no significant positve/negative correlation among the features and also with the Target"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b68248b5",
      "metadata": {
        "id": "b68248b5"
      },
      "source": [
        "### What I noticed about Numerical Features so far:\n",
        "* `BMI higher than 25 consider as Overweight`, We can observe that few people who are `OVERWEIGHTED and OBESE` have heart disease and few don't, so we created a new BMI class which was showing realistic plots so wil be using BMI class from now.\n",
        "* We can also see `Healthy Sleep Hours` can also causes heart disease like the odd cases in BMI (overweighted but don't have heart disease). SleepTime is interesting as whether they follow a `good sleeping habit or not`, `some can and some cannot escape heart disease`.\n",
        "* PhysicalHealth has positive correlation with Target and a positive correlation with MentalHealth, seems like we have multicollinearity in the dataset, which we can verify with OLS or VIF factor or using some other methods."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2598edb9",
      "metadata": {
        "id": "2598edb9"
      },
      "source": [
        "## <b>3.2 <span style='color:#0386f7de'>|</span> Categorical Feature Analysis:</b>\n",
        "\n",
        "Categorical Features:  \n",
        "`HeartDisease`, `Smoking`, `AlcoholDrinking`, `Stroke`, `DiffWalking`, `Sex`, `AgeCategory`, `Race`, `Diabetic`, `PhysicalActivity`, `GenHealth`, `Asthma`, `KidneyDisease`, `SkinCancer`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "db8dc908",
      "metadata": {
        "scrolled": true,
        "colab": {
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        },
        "id": "db8dc908",
        "outputId": "cd2ed107-013b-4206-fe87-3b971a7e9a51"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Features which has only 2 distinct values: \n",
            "['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'PhysicalActivity', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
            "\n",
            "Features which has more than 2 distinct values: \n",
            "['AgeCategory', 'Race', 'Diabetic', 'GenHealth']\n",
            "\n",
            "AgeCategory --> ['55-59' '80 or older' '65-69' '75-79' '40-44' '70-74' '60-64' '50-54'\n",
            " '45-49' '18-24' '35-39' '30-34' '25-29']\n",
            "\n",
            "Race --> ['White' 'Black' 'Asian' 'American Indian/Alaskan Native' 'Other'\n",
            " 'Hispanic']\n",
            "\n",
            "Diabetic --> ['Yes' 'No' 'No, borderline diabetes' 'Yes (during pregnancy)']\n",
            "\n",
            "GenHealth --> ['Very good' 'Fair' 'Good' 'Poor' 'Excellent']\n"
          ]
        }
      ],
      "source": [
        "# Listing down features with binary values and multiple values.\n",
        "ordinal_feat = []\n",
        "binary_feat = []\n",
        "\n",
        "[ ordinal_feat.append(i) for i in categorical_feats if df[i].nunique()>2]\n",
        "[ binary_feat.append(i) for i in categorical_feats if df[i].nunique()<=2]\n",
        "binary_feat, ordinal_feat\n",
        "print(f\"Features which has only 2 distinct values: \\n{binary_feat}\\n\")\n",
        "print(f\"Features which has more than 2 distinct values: \\n{ordinal_feat}\")\n",
        "\n",
        "for col in ordinal_feat:\n",
        "    print(f\"\\n{col} --> { df[col].unique()}\")\n",
        "    \n",
        "# Ordering the General Health classes    \n",
        "list_ordering = [\"Poor\", 'Fair', 'Good', 'Very good', 'Excellent']  \n",
        "order_type = CategoricalDtype(categories=list_ordering, ordered=True)\n",
        "df[\"GenHealth\"] = df[\"GenHealth\"].astype(order_type)  \n",
        "    \n",
        "binary_feat.remove('HeartDisease')"
      ]
    },
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        "outputId": "cb156cb5-ab1c-478f-cfa3-82aea5229066"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Distribution of Binary Features with Target\n",
        "for i in binary_feat:\n",
        "    plot_cat_dist_with_target(df, i)"
      ]
    },
    {
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      "id": "381ca93d",
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      "source": [
        "# Distribution of Ordinal Features with Target\n",
        "for i in ordinal_feat:\n",
        "    plot_cat_dist_with_target(df, i)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a9daa269",
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        "id": "a9daa269"
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      "source": [
        "### Observations (so far):\n",
        "\n",
        "1. Distribution of Smoking demonstrates that `Smokers` are more likely to get `Heart Disease`. The `actual target distribution is around ~8.5%` where as the distribution of `smokers with HeartDisease is around ~12%`, so this feature might play a good role to identify Heart Disease, which we can confirm by calculating feature importance later in this notebook.\n",
        " \n",
        "2.  Gender Distribution - shows there are `More Male` adults that have `Heart Disease` than `Female` peers.\n",
        "\n",
        "3. There is `no noticeable differences` between adults with and without heart disease in being a `heavy drinker or having asthma`.\n",
        "\n",
        "4. Nevertheless `people with heart disease` seem to `experience stroke and difficulty while walking more than` those who don’t.\n",
        "\n",
        "5. People who diagnosed with and without heart disease are `comparably distinct in physical activity` distirbution, which seems like realastic as who are doing physical activity regularly are less prone to HeartDisease.\n",
        "\n",
        "6. There might be a strong correlation between `increasing age` and the `presence of heart disease`.\n",
        "\n",
        "7. `Asian and Hispanic` responders seem to have `lower heart disease` than other peers but further analysis is necessary to confirm this statement.\n",
        "\n",
        "8. `Adults with diabetes` seem to have `high rate of having heart diseases`.\n",
        "\n",
        "9. Adults who considered `Fair or Poor general health` have `higher chance` of diagnosed with `heart diseases`.\n",
        "\n",
        "10. Distribution shows that adults with `Kidney Disease or Skin Cancer` are `Highly Prone` to Heart disease"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c2db68b0",
      "metadata": {
        "id": "c2db68b0"
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      "source": [
        "# <b>4 <span style='color:#0386f7de'>|</span> Transforming Data</b>"
      ]
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    {
      "cell_type": "markdown",
      "id": "659d4db6",
      "metadata": {
        "id": "659d4db6"
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      "source": [
        "## <b>4.1 <span style='color:#0386f7de'>|</span> Data Transformation</b>\n",
        "#### Encode Categorical Data using Dumies, and Encoders."
      ]
    },
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      "execution_count": 33,
      "id": "9a31f56f",
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              "  HeartDisease Smoking AlcoholDrinking Stroke  PhysicalHealth  MentalHealth  \\\n",
              "0           No     Yes              No     No        0.600000      0.556909   \n",
              "1           No      No              No    Yes        0.000000      0.000000   \n",
              "2           No     Yes              No     No        0.674342      0.556909   \n",
              "3           No      No              No     No        0.000000      0.000000   \n",
              "4           No      No              No     No        0.674342      0.000000   \n",
              "\n",
              "  DiffWalking     Sex  AgeCategory   Race Diabetic PhysicalActivity  \\\n",
              "0          No  Female        55-59  White      Yes              Yes   \n",
              "1          No  Female  80 or older  White       No              Yes   \n",
              "2          No    Male        65-69  White      Yes              Yes   \n",
              "3          No  Female        75-79  White       No               No   \n",
              "4         Yes  Female        40-44  White       No              Yes   \n",
              "\n",
              "   GenHealth  SleepTime Asthma KidneyDisease SkinCancer           BMI  \n",
              "0  Very good      0.250    Yes            No        Yes   UnderWeight  \n",
              "1  Very good      0.500     No            No         No  NormalWeight  \n",
              "2       Fair      0.625    Yes            No         No    OverWeight  \n",
              "3       Good      0.375     No            No        Yes  NormalWeight  \n",
              "4  Very good      0.625     No            No         No  NormalWeight  "
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              "      <th></th>\n",
              "      <th>HeartDisease</th>\n",
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              "      <td>Yes</td>\n",
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              "      <td>0.500</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NormalWeight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.674342</td>\n",
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              "      <td>65-69</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fair</td>\n",
              "      <td>0.625</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
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              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>75-79</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Good</td>\n",
              "      <td>0.375</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>No</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>Very good</td>\n",
              "      <td>0.625</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NormalWeight</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bc09e115-7521-46b7-8103-727e842e88b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "t_df = df.copy()\n",
        "t_df.drop('Target',axis=1, inplace=True)\n",
        "t_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "62aa360c",
      "metadata": {
        "scrolled": true,
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          "height": 270
        },
        "id": "62aa360c",
        "outputId": "011a6582-4a09-4e1e-a0a9-09be755ee930"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  HeartDisease Smoking AlcoholDrinking Stroke  PhysicalHealth  MentalHealth  \\\n",
              "0           No     Yes              No     No        0.600000      0.556909   \n",
              "1           No      No              No    Yes        0.000000      0.000000   \n",
              "2           No     Yes              No     No        0.674342      0.556909   \n",
              "3           No      No              No     No        0.000000      0.000000   \n",
              "4           No      No              No     No        0.674342      0.000000   \n",
              "\n",
              "  DiffWalking  AgeCategory   Race Diabetic PhysicalActivity  GenHealth  \\\n",
              "0          No        55-59  White      Yes              Yes  Very good   \n",
              "1          No  80 or older  White       No              Yes  Very good   \n",
              "2          No        65-69  White      Yes              Yes       Fair   \n",
              "3          No        75-79  White       No               No       Good   \n",
              "4         Yes        40-44  White       No              Yes  Very good   \n",
              "\n",
              "   SleepTime Asthma KidneyDisease SkinCancer           BMI  Is_Male  \n",
              "0      0.250    Yes            No        Yes   UnderWeight        0  \n",
              "1      0.500     No            No         No  NormalWeight        0  \n",
              "2      0.625    Yes            No         No    OverWeight        1  \n",
              "3      0.375     No            No        Yes  NormalWeight        0  \n",
              "4      0.625     No            No         No  NormalWeight        0  "
            ],
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              "\n",
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              "\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "t_df = pd.get_dummies(t_df, columns=['Sex'], prefix='Is' , drop_first=True)\n",
        "t_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "7b4fcbd9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b4fcbd9",
        "outputId": "a1f2a8c3-3fe6-4af2-e61b-6233afc2a9d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "White                             245212\n",
              "Hispanic                           27446\n",
              "Black                              22939\n",
              "Other                              10928\n",
              "Asian                               8068\n",
              "American Indian/Alaskan Native      5202\n",
              "Name: Race, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "t_df.Race.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "f3a44526",
      "metadata": {
        "id": "f3a44526"
      },
      "outputs": [],
      "source": [
        "# Mapping all the binary features with 1 and 0\n",
        "binary_cols = ['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke',  'DiffWalking','PhysicalActivity','Asthma','KidneyDisease','SkinCancer']\n",
        "    \n",
        "for col in binary_cols:\n",
        "    t_df[col] = t_df[col].str.upper().apply(lambda x: 1 if x =='YES' else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "1d51ca52",
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        "id": "1d51ca52",
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            "text/plain": [
              "   HeartDisease  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
              "0             0        1                0       0        0.600000   \n",
              "1             0        0                0       1        0.000000   \n",
              "2             0        1                0       0        0.674342   \n",
              "3             0        0                0       0        0.000000   \n",
              "4             0        0                0       0        0.674342   \n",
              "\n",
              "   MentalHealth  DiffWalking  AgeCategory   Race Diabetic  PhysicalActivity  \\\n",
              "0      0.556909            0            7  White      Yes                 1   \n",
              "1      0.000000            0           12  White       No                 1   \n",
              "2      0.556909            0            9  White      Yes                 1   \n",
              "3      0.000000            0           11  White       No                 0   \n",
              "4      0.000000            1            4  White       No                 1   \n",
              "\n",
              "   GenHealth  SleepTime  Asthma  KidneyDisease  SkinCancer  BMI  Is_Male  \n",
              "0          3      0.250       1              0           1    0        0  \n",
              "1          3      0.500       0              0           0    1        0  \n",
              "2          1      0.625       1              0           0    2        1  \n",
              "3          2      0.375       0              0           1    1        0  \n",
              "4          3      0.625       0              0           0    1        0  "
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            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "#Ordinal encoding for order variableslike AgeCategory, GenHealth, BMI\n",
        "\n",
        "age_encoder= ce.OrdinalEncoder(cols=['AgeCategory'],return_df=True,\n",
        "                               mapping=[{'col':'AgeCategory',\n",
        "                              'mapping':{'18-24':0, '25-29':1,'30-34':2,'35-39':3,'40-44':4,'45-49':5,'50-54':6,'55-59':7,'60-64':8,'65-69':9,'70-74':10,'75-79':11,'80 or older':12}}])\n",
        "\n",
        "\n",
        "health_encoder = ce.OrdinalEncoder(cols=['GenHealth'], return_df=True, mapping=[{'col':'GenHealth',\n",
        " 'mapping':{'Poor':0,'Fair':1,'Good':2,'Very good':3,'Excellent':4}}])\n",
        "\n",
        "BMI_encoder = ce.OrdinalEncoder(cols=['BMI'], return_df=True, mapping=[{'col':'BMI','mapping':{'UnderWeight':0,'NormalWeight':1,'OverWeight':2,'Obesity Class I':3,'Obesity Class II':4, 'Obesity Class III':5}}])\n",
        "\n",
        "t_df['AgeCategory'] = age_encoder.fit_transform(t_df['AgeCategory'])\n",
        "t_df['GenHealth'] = health_encoder.fit_transform(t_df['GenHealth'])\n",
        "t_df['BMI'] = BMI_encoder.fit_transform(t_df['BMI'])\n",
        "\n",
        "t_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa3a78c6",
      "metadata": {
        "id": "fa3a78c6"
      },
      "source": [
        "* Why I have used Ordinal Encoder for Age, Health, BMI? \n",
        "    * I have categories in each feature which have ordered relationship between each categories, so OrdinalEncoder does that job by using given ordered map\n",
        "     \n",
        "* Why OneHot Encoder for Race and Diabetic?\n",
        "    * As my feature values do not have any order among each other and few categories have high impact on identifying heart Disease like Diabetic-YES, White Race adults have high rate of HeartDisease... One hot encoder creates new features using feature categories and they have binary values in each feature which helps in identifying direct impact on Target but only issue with oneHotEncoder is it increase the number of features.  [OneHotEncoder is not Recommender for High Cardinal Features and Ordinal Features as high cardinality increases Total number of features in the dataset]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "f6d3d30a",
      "metadata": {
        "id": "f6d3d30a"
      },
      "outputs": [],
      "source": [
        "# # One Hot Encoding for vaiables with multiple values like Race, Diabetic\n",
        "\n",
        "encoder_race=ce.OneHotEncoder(cols='Race',handle_unknown='return_nan',return_df=True,use_cat_names=True)\n",
        "encoder_diabetic = ce.OneHotEncoder(cols='Diabetic', handle_unknown='return_nan', return_df=True, use_cat_names=True)\n",
        "\n",
        "t_df = encoder_race.fit_transform(t_df)\n",
        "t_df = encoder_diabetic.fit_transform(t_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "7728754a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "7728754a",
        "outputId": "6674684b-2b69-4552-d6aa-2c3d10704284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HeartDisease  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
              "0             0        1                0       0        0.600000   \n",
              "1             0        0                0       1        0.000000   \n",
              "2             0        1                0       0        0.674342   \n",
              "3             0        0                0       0        0.000000   \n",
              "4             0        0                0       0        0.674342   \n",
              "\n",
              "   MentalHealth  DiffWalking  AgeCategory  Race_White  Race_Black  Race_Asian  \\\n",
              "0      0.556909            0            7         1.0         0.0         0.0   \n",
              "1      0.000000            0           12         1.0         0.0         0.0   \n",
              "2      0.556909            0            9         1.0         0.0         0.0   \n",
              "3      0.000000            0           11         1.0         0.0         0.0   \n",
              "4      0.000000            1            4         1.0         0.0         0.0   \n",
              "\n",
              "   Race_American Indian/Alaskan Native  Race_Other  Race_Hispanic  \\\n",
              "0                                  0.0         0.0            0.0   \n",
              "1                                  0.0         0.0            0.0   \n",
              "2                                  0.0         0.0            0.0   \n",
              "3                                  0.0         0.0            0.0   \n",
              "4                                  0.0         0.0            0.0   \n",
              "\n",
              "   Diabetic_Yes  Diabetic_No  Diabetic_No, borderline diabetes  \\\n",
              "0           1.0          0.0                               0.0   \n",
              "1           0.0          1.0                               0.0   \n",
              "2           1.0          0.0                               0.0   \n",
              "3           0.0          1.0                               0.0   \n",
              "4           0.0          1.0                               0.0   \n",
              "\n",
              "   Diabetic_Yes (during pregnancy)  PhysicalActivity  GenHealth  SleepTime  \\\n",
              "0                              0.0                 1          3      0.250   \n",
              "1                              0.0                 1          3      0.500   \n",
              "2                              0.0                 1          1      0.625   \n",
              "3                              0.0                 0          2      0.375   \n",
              "4                              0.0                 1          3      0.625   \n",
              "\n",
              "   Asthma  KidneyDisease  SkinCancer  BMI  Is_Male  \n",
              "0       1              0           1    0        0  \n",
              "1       0              0           0    1        0  \n",
              "2       1              0           0    2        1  \n",
              "3       0              0           1    1        0  \n",
              "4       0              0           0    1        0  "
            ],
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d91fb954-8f27-4de3-8666-0f6469fc8c0f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d91fb954-8f27-4de3-8666-0f6469fc8c0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "t_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f6ca15f2",
      "metadata": {
        "id": "f6ca15f2"
      },
      "source": [
        "## <b>4.2 <span style='color:#0386f7de'>|</span> Feature Importance</b>\n",
        "#### To find features which have strong correlation with our Target and to remove multicollinearity (if present in the dataset)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "58795650",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "58795650",
        "outputId": "bd21bde0-6801-4774-b44a-17432e98fcea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1900x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "data_corr = t_df.corr()\n",
        "mask = np.triu(np.ones_like(t_df.corr(), dtype=bool))\n",
        "\n",
        "corr_ft = plt.figure(figsize= (19, 10))\n",
        "corr_ft = sns.heatmap(data_corr, mask=mask,vmin= -1, vmax = 1, annot=True, linewidths= 0.3, cmap= \"BrBG\")\n",
        "corr_ft.set_title(\"The Pearson Correlation between Features\",\n",
        "                   fontsize= 15,\n",
        "                   pad= 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a5f40a0a",
      "metadata": {
        "id": "a5f40a0a"
      },
      "source": [
        "#### Observations: \n",
        "1. There seems multicollinearity exists among some of the features like `PhysicalHealth, MentalHealth, DiffWalking` with `GenHealth`, as if teh adults are suffering with physical illness or mental illness for many days their genHealth will be poor and adults find with difficult with Walking are not very good in their health condition.\n",
        "2. `DiffWalking` also has significant negative correlation with `PhysicalAcitivity`, as expected adults who are regularly doing physical activity wont find any difficulty in walking as they will be active.\n",
        "3. Some of our Race features are correlated with other Race features, so lets consider only Race_white as it has high weightage compare to other race features with Target.\n",
        "4. `Diabetic_Yes` and `Diabetic_No` are highly negatively correlated as both are very realted, so lets consider `Diabetic_Yes`. `Diabetic_Yes` is again correlated with GenHealth, so lets confirm the multicollinearity using VIF or OLS methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "376801d5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "376801d5",
        "outputId": "24834c6f-e116-4abc-ddbc-fd8d11fe9527"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Features  FeatureWeights\n",
              "0                        AgeCategory        0.233432\n",
              "1                        DiffWalking        0.201258\n",
              "2                             Stroke        0.196835\n",
              "3                       Diabetic_Yes        0.183072\n",
              "4                      KidneyDisease        0.145197\n",
              "5                     PhysicalHealth        0.136415\n",
              "6                            Smoking        0.107764\n",
              "7                         SkinCancer        0.093317\n",
              "8                            Is_Male        0.070040\n",
              "9                                BMI        0.052175\n",
              "10                            Asthma        0.041444\n",
              "11                        Race_White        0.040121\n",
              "12  Diabetic_No, borderline diabetes        0.016182\n",
              "13                         SleepTime       -0.003802\n",
              "14   Diabetic_Yes (during pregnancy)       -0.013930\n",
              "15                   AlcoholDrinking       -0.032080\n",
              "16                         GenHealth       -0.243182"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3630756-e634-4f4a-8e15-393bfb863d4a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Features</th>\n",
              "      <th>FeatureWeights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AgeCategory</td>\n",
              "      <td>0.233432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DiffWalking</td>\n",
              "      <td>0.201258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Stroke</td>\n",
              "      <td>0.196835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Diabetic_Yes</td>\n",
              "      <td>0.183072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KidneyDisease</td>\n",
              "      <td>0.145197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>PhysicalHealth</td>\n",
              "      <td>0.136415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Smoking</td>\n",
              "      <td>0.107764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SkinCancer</td>\n",
              "      <td>0.093317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Is_Male</td>\n",
              "      <td>0.070040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BMI</td>\n",
              "      <td>0.052175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Asthma</td>\n",
              "      <td>0.041444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Race_White</td>\n",
              "      <td>0.040121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Diabetic_No, borderline diabetes</td>\n",
              "      <td>0.016182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SleepTime</td>\n",
              "      <td>-0.003802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Diabetic_Yes (during pregnancy)</td>\n",
              "      <td>-0.013930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AlcoholDrinking</td>\n",
              "      <td>-0.032080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>GenHealth</td>\n",
              "      <td>-0.243182</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3630756-e634-4f4a-8e15-393bfb863d4a')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-e3630756-e634-4f4a-8e15-393bfb863d4a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-e3630756-e634-4f4a-8e15-393bfb863d4a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Selecting independent features and computing their feature weights using Pearson correlation\n",
        "drop_feat = ['MentalHealth', 'PhysicalActivity', 'Race_Black', 'Race_Asian',\n",
        "             \"Race_American Indian/Alaskan Native\", 'Race_Other', 'Race_Hispanic', 'Diabetic_No']\n",
        "\n",
        "t_df_imp = t_df.drop(drop_feat,axis=1).copy()\n",
        "\n",
        "data_corr = t_df_imp.corr()\n",
        "\n",
        "fwt = pd.DataFrame(data_corr['HeartDisease'].values, columns=['FeatureWeights'])\n",
        "# fwt['Features'] = data_corr['HeartDisease'].index\n",
        "fwt.insert(loc=0, column='Features', value=data_corr['HeartDisease'].index)\n",
        "fwt = fwt.loc[1:,:]\n",
        "fwt = fwt.sort_values('FeatureWeights', ascending = False).reset_index(drop=True)\n",
        "fwt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4002906a",
      "metadata": {
        "id": "4002906a"
      },
      "source": [
        "### Feature Importance using Pearson Correlation:\n",
        "\n",
        "* AgeCategory, Stroke, Diabetic_yes have very positive correlation with Target where as AlcholDrinking and GenHealth are very negatively correlated with Target.\n",
        "* List of Important Features -->  `['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'is_Male', 'BMI', 'Asthma', 'Race_White', 'Diabetic_No, borderline diabetes', 'SleepTime', 'Diabetic_Yes (during pregnancy)', 'AlcoholDrinking', 'GenHealth']`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "09af3257",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "09af3257",
        "outputId": "0506609b-4c9e-4ba8-896e-32cf1c7865d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "corr_ft_hd_x = list(data_corr.columns)\n",
        "del corr_ft_hd_x[0]\n",
        "corr_ft_hd_y = list(data_corr[\"HeartDisease\"])\n",
        "del corr_ft_hd_y[0]\n",
        "\n",
        "corr_ft_hd = plt.figure(figsize= (16, 8))\n",
        "corr_ft_hd = sns.barplot(x= corr_ft_hd_x, y= corr_ft_hd_y, palette= \"BrBG\")\n",
        "corr_ft_hd.set_title(\"Pearson Correlation between Each Feature and Heart Disease\",\n",
        "                     fontsize= 15, pad= 12)\n",
        "corr_ft_hd.set(xlabel= \"Possible Factors\",\n",
        "               ylabel= \"Pearson Correaltion Coefficient\",\n",
        "               ylim= (-0.3, 0.3))\n",
        "corr_ft_hd.set_xticklabels(corr_ft_hd_x, rotation= \"vertical\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b441d83",
      "metadata": {
        "id": "5b441d83"
      },
      "source": [
        "### 2. OLS Method: Let's check significant features and their weights using OLS Method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "c67edb51",
      "metadata": {
        "id": "c67edb51"
      },
      "outputs": [],
      "source": [
        "# Defining a function to calculate feature coefficients with p-values using ols method.\n",
        "import statsmodels.api as sm\n",
        "\n",
        "def cal_OLS_summary(t_df):\n",
        "    col = t_df.columns.tolist()\n",
        "    col.remove('HeartDisease')\n",
        "    col\n",
        "\n",
        "    model = sm.OLS(t_df['HeartDisease'], t_df[col]).fit()\n",
        "    # Print out the statistics\n",
        "    return model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "678ca005",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "id": "678ca005",
        "outputId": "d26deeeb-65b8-4747-9d64-941a1306c914"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:           HeartDisease   R-squared:                       0.141\n",
              "Model:                            OLS   Adj. R-squared:                  0.141\n",
              "Method:                 Least Squares   F-statistic:                     2279.\n",
              "Date:                Tue, 25 Apr 2023   Prob (F-statistic):               0.00\n",
              "Time:                        16:47:08   Log-Likelihood:                -22143.\n",
              "No. Observations:              319795   AIC:                         4.433e+04\n",
              "Df Residuals:                  319771   BIC:                         4.459e+04\n",
              "Df Model:                          23                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=======================================================================================================\n",
              "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------------------------------\n",
              "Smoking                                 0.0201      0.001     20.748      0.000       0.018       0.022\n",
              "AlcoholDrinking                        -0.0155      0.002     -8.386      0.000      -0.019      -0.012\n",
              "Stroke                                  0.1835      0.002     74.110      0.000       0.179       0.188\n",
              "PhysicalHealth                          0.0237      0.002     13.255      0.000       0.020       0.027\n",
              "MentalHealth                           -0.0012      0.002     -0.648      0.517      -0.005       0.002\n",
              "DiffWalking                             0.0466      0.002     29.956      0.000       0.044       0.050\n",
              "AgeCategory                             0.0113      0.000     74.149      0.000       0.011       0.012\n",
              "Race_White                              0.0296      0.002     18.184      0.000       0.026       0.033\n",
              "Race_Black                              0.0104      0.002      4.966      0.000       0.006       0.015\n",
              "Race_Asian                              0.0174      0.003      5.994      0.000       0.012       0.023\n",
              "Race_American Indian/Alaskan Native     0.0294      0.004      8.314      0.000       0.022       0.036\n",
              "Race_Other                              0.0291      0.003     11.007      0.000       0.024       0.034\n",
              "Race_Hispanic                           0.0204      0.002     10.448      0.000       0.017       0.024\n",
              "Diabetic_Yes                            0.0777      0.002     33.744      0.000       0.073       0.082\n",
              "Diabetic_No                             0.0143      0.002      7.236      0.000       0.010       0.018\n",
              "Diabetic_No, borderline diabetes        0.0225      0.003      6.548      0.000       0.016       0.029\n",
              "Diabetic_Yes (during pregnancy)         0.0219      0.005      4.425      0.000       0.012       0.032\n",
              "PhysicalActivity                       -0.0029      0.001     -2.490      0.013      -0.005      -0.001\n",
              "GenHealth                              -0.0328      0.001    -59.674      0.000      -0.034      -0.032\n",
              "SleepTime                               0.0022      0.003      0.709      0.478      -0.004       0.008\n",
              "Asthma                                  0.0159      0.001     11.545      0.000       0.013       0.019\n",
              "KidneyDisease                           0.1015      0.003     40.498      0.000       0.097       0.106\n",
              "SkinCancer                              0.0249      0.002     15.140      0.000       0.022       0.028\n",
              "BMI                                    -0.0033      0.000     -7.827      0.000      -0.004      -0.002\n",
              "Is_Male                                 0.0482      0.001     50.910      0.000       0.046       0.050\n",
              "==============================================================================\n",
              "Omnibus:                   150879.432   Durbin-Watson:                   1.997\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           689378.163\n",
              "Skew:                           2.375   Prob(JB):                         0.00\n",
              "Kurtosis:                       8.401   Cond. No.                     2.83e+16\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The smallest eigenvalue is 2.65e-26. This might indicate that there are\n",
              "strong multicollinearity problems or that the design matrix is singular.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>HeartDisease</td>   <th>  R-squared:         </th> <td>   0.141</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.141</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2279.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 25 Apr 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>16:47:08</td>     <th>  Log-Likelihood:    </th> <td> -22143.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>319795</td>      <th>  AIC:               </th> <td>4.433e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>319771</td>      <th>  BIC:               </th> <td>4.459e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    23</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                   <td></td>                      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Smoking</th>                             <td>    0.0201</td> <td>    0.001</td> <td>   20.748</td> <td> 0.000</td> <td>    0.018</td> <td>    0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AlcoholDrinking</th>                     <td>   -0.0155</td> <td>    0.002</td> <td>   -8.386</td> <td> 0.000</td> <td>   -0.019</td> <td>   -0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Stroke</th>                              <td>    0.1835</td> <td>    0.002</td> <td>   74.110</td> <td> 0.000</td> <td>    0.179</td> <td>    0.188</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PhysicalHealth</th>                      <td>    0.0237</td> <td>    0.002</td> <td>   13.255</td> <td> 0.000</td> <td>    0.020</td> <td>    0.027</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MentalHealth</th>                        <td>   -0.0012</td> <td>    0.002</td> <td>   -0.648</td> <td> 0.517</td> <td>   -0.005</td> <td>    0.002</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>DiffWalking</th>                         <td>    0.0466</td> <td>    0.002</td> <td>   29.956</td> <td> 0.000</td> <td>    0.044</td> <td>    0.050</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AgeCategory</th>                         <td>    0.0113</td> <td>    0.000</td> <td>   74.149</td> <td> 0.000</td> <td>    0.011</td> <td>    0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_White</th>                          <td>    0.0296</td> <td>    0.002</td> <td>   18.184</td> <td> 0.000</td> <td>    0.026</td> <td>    0.033</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_Black</th>                          <td>    0.0104</td> <td>    0.002</td> <td>    4.966</td> <td> 0.000</td> <td>    0.006</td> <td>    0.015</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_Asian</th>                          <td>    0.0174</td> <td>    0.003</td> <td>    5.994</td> <td> 0.000</td> <td>    0.012</td> <td>    0.023</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_American Indian/Alaskan Native</th> <td>    0.0294</td> <td>    0.004</td> <td>    8.314</td> <td> 0.000</td> <td>    0.022</td> <td>    0.036</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_Other</th>                          <td>    0.0291</td> <td>    0.003</td> <td>   11.007</td> <td> 0.000</td> <td>    0.024</td> <td>    0.034</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_Hispanic</th>                       <td>    0.0204</td> <td>    0.002</td> <td>   10.448</td> <td> 0.000</td> <td>    0.017</td> <td>    0.024</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_Yes</th>                        <td>    0.0777</td> <td>    0.002</td> <td>   33.744</td> <td> 0.000</td> <td>    0.073</td> <td>    0.082</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_No</th>                         <td>    0.0143</td> <td>    0.002</td> <td>    7.236</td> <td> 0.000</td> <td>    0.010</td> <td>    0.018</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_No, borderline diabetes</th>    <td>    0.0225</td> <td>    0.003</td> <td>    6.548</td> <td> 0.000</td> <td>    0.016</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_Yes (during pregnancy)</th>     <td>    0.0219</td> <td>    0.005</td> <td>    4.425</td> <td> 0.000</td> <td>    0.012</td> <td>    0.032</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PhysicalActivity</th>                    <td>   -0.0029</td> <td>    0.001</td> <td>   -2.490</td> <td> 0.013</td> <td>   -0.005</td> <td>   -0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>GenHealth</th>                           <td>   -0.0328</td> <td>    0.001</td> <td>  -59.674</td> <td> 0.000</td> <td>   -0.034</td> <td>   -0.032</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>SleepTime</th>                           <td>    0.0022</td> <td>    0.003</td> <td>    0.709</td> <td> 0.478</td> <td>   -0.004</td> <td>    0.008</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Asthma</th>                              <td>    0.0159</td> <td>    0.001</td> <td>   11.545</td> <td> 0.000</td> <td>    0.013</td> <td>    0.019</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>KidneyDisease</th>                       <td>    0.1015</td> <td>    0.003</td> <td>   40.498</td> <td> 0.000</td> <td>    0.097</td> <td>    0.106</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>SkinCancer</th>                          <td>    0.0249</td> <td>    0.002</td> <td>   15.140</td> <td> 0.000</td> <td>    0.022</td> <td>    0.028</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>BMI</th>                                 <td>   -0.0033</td> <td>    0.000</td> <td>   -7.827</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Is_Male</th>                             <td>    0.0482</td> <td>    0.001</td> <td>   50.910</td> <td> 0.000</td> <td>    0.046</td> <td>    0.050</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>150879.432</td> <th>  Durbin-Watson:     </th>  <td>   1.997</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>689378.163</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>            <td> 2.375</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>        <td> 8.401</td>   <th>  Cond. No.          </th>  <td>2.83e+16</td> \n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 2.65e-26. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "cal_OLS_summary(t_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "13aefedd",
      "metadata": {
        "id": "13aefedd"
      },
      "source": [
        "### Note:\n",
        "Even OLS results confirms the multicollinearity among the features, so lets select same features as we selected using Pearson correlation and check for collinearity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "4a781c5a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "4a781c5a",
        "outputId": "d3792455-d872-4842-e8d0-bd03c785b6f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                 OLS Regression Results                                \n",
              "=======================================================================================\n",
              "Dep. Variable:           HeartDisease   R-squared (uncentered):                   0.214\n",
              "Model:                            OLS   Adj. R-squared (uncentered):              0.214\n",
              "Method:                 Least Squares   F-statistic:                              5120.\n",
              "Date:                Tue, 25 Apr 2023   Prob (F-statistic):                        0.00\n",
              "Time:                        16:47:09   Log-Likelihood:                         -22231.\n",
              "No. Observations:              319795   AIC:                                  4.450e+04\n",
              "Df Residuals:                  319778   BIC:                                  4.468e+04\n",
              "Df Model:                          17                                                  \n",
              "Covariance Type:            nonrobust                                                  \n",
              "====================================================================================================\n",
              "                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
              "----------------------------------------------------------------------------------------------------\n",
              "Smoking                              0.0222      0.001     23.216      0.000       0.020       0.024\n",
              "AlcoholDrinking                     -0.0144      0.002     -7.849      0.000      -0.018      -0.011\n",
              "Stroke                               0.1844      0.002     74.495      0.000       0.180       0.189\n",
              "PhysicalHealth                       0.0294      0.002     17.451      0.000       0.026       0.033\n",
              "DiffWalking                          0.0489      0.002     31.914      0.000       0.046       0.052\n",
              "AgeCategory                          0.0118      0.000     83.376      0.000       0.011       0.012\n",
              "Race_White                           0.0126      0.001     11.387      0.000       0.010       0.015\n",
              "Diabetic_Yes                         0.0641      0.001     42.951      0.000       0.061       0.067\n",
              "Diabetic_No, borderline diabetes     0.0092      0.003      2.880      0.004       0.003       0.016\n",
              "Diabetic_Yes (during pregnancy)      0.0105      0.005      2.038      0.042       0.000       0.021\n",
              "GenHealth                           -0.0293      0.000    -68.257      0.000      -0.030      -0.028\n",
              "SleepTime                            0.0193      0.003      7.334      0.000       0.014       0.024\n",
              "Asthma                               0.0179      0.001     13.081      0.000       0.015       0.021\n",
              "KidneyDisease                        0.1026      0.003     40.941      0.000       0.098       0.108\n",
              "SkinCancer                           0.0242      0.002     14.709      0.000       0.021       0.027\n",
              "BMI                                 -0.0014      0.000     -3.633      0.000      -0.002      -0.001\n",
              "Is_Male                              0.0502      0.001     54.404      0.000       0.048       0.052\n",
              "==============================================================================\n",
              "Omnibus:                   150418.098   Durbin-Watson:                   1.997\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           684407.730\n",
              "Skew:                           2.368   Prob(JB):                         0.00\n",
              "Kurtosis:                       8.378   Cond. No.                         91.1\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
              "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>HeartDisease</td>   <th>  R-squared (uncentered):</th>      <td>   0.214</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.214</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   5120.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 25 Apr 2023</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>16:47:09</td>     <th>  Log-Likelihood:    </th>          <td> -22231.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>319795</td>      <th>  AIC:               </th>          <td>4.450e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>319778</td>      <th>  BIC:               </th>          <td>4.468e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    17</td>      <th>                     </th>              <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                  <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Smoking</th>                          <td>    0.0222</td> <td>    0.001</td> <td>   23.216</td> <td> 0.000</td> <td>    0.020</td> <td>    0.024</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AlcoholDrinking</th>                  <td>   -0.0144</td> <td>    0.002</td> <td>   -7.849</td> <td> 0.000</td> <td>   -0.018</td> <td>   -0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Stroke</th>                           <td>    0.1844</td> <td>    0.002</td> <td>   74.495</td> <td> 0.000</td> <td>    0.180</td> <td>    0.189</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PhysicalHealth</th>                   <td>    0.0294</td> <td>    0.002</td> <td>   17.451</td> <td> 0.000</td> <td>    0.026</td> <td>    0.033</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>DiffWalking</th>                      <td>    0.0489</td> <td>    0.002</td> <td>   31.914</td> <td> 0.000</td> <td>    0.046</td> <td>    0.052</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AgeCategory</th>                      <td>    0.0118</td> <td>    0.000</td> <td>   83.376</td> <td> 0.000</td> <td>    0.011</td> <td>    0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Race_White</th>                       <td>    0.0126</td> <td>    0.001</td> <td>   11.387</td> <td> 0.000</td> <td>    0.010</td> <td>    0.015</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_Yes</th>                     <td>    0.0641</td> <td>    0.001</td> <td>   42.951</td> <td> 0.000</td> <td>    0.061</td> <td>    0.067</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_No, borderline diabetes</th> <td>    0.0092</td> <td>    0.003</td> <td>    2.880</td> <td> 0.004</td> <td>    0.003</td> <td>    0.016</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Diabetic_Yes (during pregnancy)</th>  <td>    0.0105</td> <td>    0.005</td> <td>    2.038</td> <td> 0.042</td> <td>    0.000</td> <td>    0.021</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>GenHealth</th>                        <td>   -0.0293</td> <td>    0.000</td> <td>  -68.257</td> <td> 0.000</td> <td>   -0.030</td> <td>   -0.028</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>SleepTime</th>                        <td>    0.0193</td> <td>    0.003</td> <td>    7.334</td> <td> 0.000</td> <td>    0.014</td> <td>    0.024</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Asthma</th>                           <td>    0.0179</td> <td>    0.001</td> <td>   13.081</td> <td> 0.000</td> <td>    0.015</td> <td>    0.021</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>KidneyDisease</th>                    <td>    0.1026</td> <td>    0.003</td> <td>   40.941</td> <td> 0.000</td> <td>    0.098</td> <td>    0.108</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>SkinCancer</th>                       <td>    0.0242</td> <td>    0.002</td> <td>   14.709</td> <td> 0.000</td> <td>    0.021</td> <td>    0.027</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>BMI</th>                              <td>   -0.0014</td> <td>    0.000</td> <td>   -3.633</td> <td> 0.000</td> <td>   -0.002</td> <td>   -0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Is_Male</th>                          <td>    0.0502</td> <td>    0.001</td> <td>   54.404</td> <td> 0.000</td> <td>    0.048</td> <td>    0.052</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>150418.098</td> <th>  Durbin-Watson:     </th>  <td>   1.997</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>684407.730</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>            <td> 2.368</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>        <td> 8.378</td>   <th>  Cond. No.          </th>  <td>    91.1</td> \n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "cal_OLS_summary(t_df_imp)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed3b65ea",
      "metadata": {
        "id": "ed3b65ea"
      },
      "source": [
        "##### Points to Consider:\n",
        "Selected features do not have the multicollinearity among the features and OLS results confirms it.\n",
        "\n",
        "* As we are trying to predict the Heart Disease and it is a critical case, I am assuming my significance level as 1% (p=0.01 as threshold).\n",
        "* All the selected features are `significant` except `Diabetic_Yes (during pregnancy)`.\n",
        "\n",
        "* List of Important Features -->  `['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'is_Male', 'BMI', 'Asthma', 'Race_White', 'Diabetic_No, borderline diabetes', 'SleepTime',  'AlcoholDrinking', 'GenHealth']`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "977eece4",
      "metadata": {
        "id": "977eece4"
      },
      "source": [
        "### 3. Permutation Importance : Let's calculate Feature weights using Permutation Importance."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install eli5"
      ],
      "metadata": {
        "id": "AVDZBcZT-mcK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9d5576e-9d43-4217-ee63-a6a476a1eab9"
      },
      "id": "AVDZBcZT-mcK",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting eli5\n",
            "  Downloading eli5-0.13.0.tar.gz (216 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m216.2/216.2 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: attrs>17.1.0 in /usr/local/lib/python3.9/dist-packages (from eli5) (23.1.0)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.20->eli5) (1.2.0)\n",
            "Building wheels for collected packages: eli5\n",
            "  Building wheel for eli5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for eli5: filename=eli5-0.13.0-py2.py3-none-any.whl size=107747 sha256=b9c4e5e4085f5e7a7ae5c4db0b81d2aa22e5203118e6fac0f3d836aa2a474bbd\n",
            "  Stored in directory: /root/.cache/pip/wheels/7b/26/a5/8460416695a992a2966b41caa5338e5e7fcea98c9d032d055c\n",
            "Successfully built eli5\n",
            "Installing collected packages: eli5\n",
            "Successfully installed eli5-0.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "1bcabcc9",
      "metadata": {
        "id": "1bcabcc9"
      },
      "outputs": [],
      "source": [
        "# Define own function to calculate PermutationImportance of given data\n",
        "from sklearn.model_selection import  train_test_split\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance\n",
        "\n",
        "def cal_PermutationImportance_df(t_df):\n",
        "    feat = t_df.columns.tolist()\n",
        "    feat.remove('HeartDisease')\n",
        "    X = t_df[feat].copy()\n",
        "    y = t_df['HeartDisease']\n",
        "    #Spliting data into Training 75%,  Test set 10% and Validation set 15%.\n",
        "\n",
        "    X_t, X_test, y_t, y_test = train_test_split(X, y, test_size=0.15, random_state=1)\n",
        "    X_train, X_val, y_train, y_val = train_test_split(X_t, y_t, test_size=0.10, random_state=1)\n",
        "    \n",
        "    # Create linear regression object\n",
        "#     model = linear_model.LogisticRegression()\n",
        "    model = linear_model.LinearRegression()\n",
        "    model.fit(X_train,y_train)\n",
        "\n",
        "    pi = PermutationImportance(model, random_state=1).fit(X_test, y_test)\n",
        "    return eli5.show_weights(pi, feature_names = X_test.columns.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "7ac88bac",
      "metadata": {
        "id": "7ac88bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "69ec6ad5-a477-461b-b7cb-1070d71160b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <style>\n",
              "    table.eli5-weights tr:hover {\n",
              "        filter: brightness(85%);\n",
              "    }\n",
              "</style>\n",
              "\n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
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              "    \n",
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              "\n",
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              "\n",
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              "\n",
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              "    \n",
              "\n",
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              "\n",
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              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
              "    <thead>\n",
              "    <tr style=\"border: none;\">\n",
              "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
              "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0477\n",
              "                \n",
              "                    &plusmn; 0.0022\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                GenHealth\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 80.09%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0474\n",
              "                \n",
              "                    &plusmn; 0.0027\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                AgeCategory\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 84.50%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0331\n",
              "                \n",
              "                    &plusmn; 0.0027\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Stroke\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 91.88%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0132\n",
              "                \n",
              "                    &plusmn; 0.0019\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Is_Male\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 92.06%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0127\n",
              "                \n",
              "                    &plusmn; 0.0013\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Diabetic_Yes\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 92.63%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0115\n",
              "                \n",
              "                    &plusmn; 0.0019\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                KidneyDisease\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 96.92%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0033\n",
              "                \n",
              "                    &plusmn; 0.0008\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Smoking\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 98.69%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0010\n",
              "                \n",
              "                    &plusmn; 0.0006\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                SkinCancer\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 98.70%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0010\n",
              "                \n",
              "                    &plusmn; 0.0004\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Asthma\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 99.21%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0005\n",
              "                \n",
              "                    &plusmn; 0.0001\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                AlcoholDrinking\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 99.27%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0004\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Race_White\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 99.66%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0001\n",
              "                \n",
              "                    &plusmn; 0.0001\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                BMI\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "    \n",
              "    </tbody>\n",
              "</table>\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
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              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "imp_feats_to_check = [\"HeartDisease\", 'AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']\n",
        "\n",
        "cal_PermutationImportance_df(t_df_imp[imp_feats_to_check])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a602edb",
      "metadata": {
        "id": "4a602edb"
      },
      "source": [
        "### Is the predictor variables independent of all the other predictor variables?\n",
        "\n",
        "##### Observations: \n",
        "1. There seems multicollinearity exists among some of the features like `PhysicalHealth, MentalHealth, DiffWalking` with `GenHealth`, as if teh adults are suffering with physical illness or mental illness for many days their genHealth will be poor and adults find with difficult with Walking are not very good in their health condition.\n",
        "2. `DiffWalking` also has significant negative correlation with `PhysicalAcitivity`, as expected adults who are regularly doing physical activity wont find any difficulty in walking as they will be active.\n",
        "3. Some of our Race features are correlated with other Race features, so lets consider only Race_white as it has high weightage compare to other race features with Target.\n",
        "4. `Diabetic_Yes` and `Diabetic_No` are highly negatively correlated as both are very realted, so lets consider `Diabetic_Yes`. `Diabetic_Yes` is again correlated with GenHealth, so lets confirm the multicollinearity using VIF or OLS methods.\n",
        "\n",
        "* Feature Importance of PermutationImportance aligns with above 2 methods (Pearson Correlation weightage and OLS Method), As excpected AgeCategory, GenHealth, Stroke, Diabets plays significant role to identify HeartDisease.\n",
        "\n",
        "* `SleepTime, Diabetic During Pregnancy and Borderline Diabetes` are `not having any significant importance` with any of above tried 3 methods, it is `better to drop them`.\n",
        "\n",
        "* Final List of Selected Important Features/Variables which are independent of each other -->  `['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'is_Male', 'BMI', 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee58c109",
      "metadata": {
        "id": "ee58c109"
      },
      "source": [
        "### Which independent variables are useful to predict a target (dependent variable)?\n",
        "* Below list of Selected Important Features/Variables are independent of each other and have significance importance for each feature to predict HeartDisease. \n",
        "* `['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'is_Male', 'BMI', 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']`\n",
        "* We also checked the multicollinearity of these features using VarianceInfliationFactor and it confirms that there are no dependent features among above selected features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "d88d7f62",
      "metadata": {
        "scrolled": true,
        "id": "d88d7f62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f5d8297-e719-40b3-ca4b-8ad4dc5c75bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking % Missing Data in Important Features:\n",
            "AgeCategory        0.0\n",
            "Stroke             0.0\n",
            "Diabetic_Yes       0.0\n",
            "KidneyDisease      0.0\n",
            "Smoking            0.0\n",
            "SkinCancer         0.0\n",
            "Is_Male            0.0\n",
            "BMI                0.0\n",
            "Asthma             0.0\n",
            "Race_White         0.0\n",
            "AlcoholDrinking    0.0\n",
            "GenHealth          0.0\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "imp_feats = ['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']\n",
        "print(f\"Checking % Missing Data in Important Features:\\n{100*t_df_imp[imp_feats].isnull().sum()/t_df_imp.shape[0]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c6e1b12",
      "metadata": {
        "id": "0c6e1b12"
      },
      "source": [
        "### Lets check the multicollinearity using Variance Inflation Factor (VIF) on above selected features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "e6f98e5f",
      "metadata": {
        "id": "e6f98e5f"
      },
      "outputs": [],
      "source": [
        "# load statmodels functions to calculate variance_inflation_factor -- to check multicollinearity\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "def calc_vif(X):\n",
        "    vif = pd.DataFrame()\n",
        "    vif[\"variables\"] = X.columns\n",
        "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
        "    vif.sort_values('VIF',inplace=True,ascending=False)\n",
        "    return(vif)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "32fc9d3c",
      "metadata": {
        "id": "32fc9d3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "df6c7e0d-0c26-4e78-fbe4-e9f83b1ccda5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HeartDisease  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
              "0             0        1                0       0        0.600000   \n",
              "1             0        0                0       1        0.000000   \n",
              "2             0        1                0       0        0.674342   \n",
              "3             0        0                0       0        0.000000   \n",
              "4             0        0                0       0        0.674342   \n",
              "\n",
              "   DiffWalking  AgeCategory  Race_White  Diabetic_Yes  \\\n",
              "0            0            7         1.0           1.0   \n",
              "1            0           12         1.0           0.0   \n",
              "2            0            9         1.0           1.0   \n",
              "3            0           11         1.0           0.0   \n",
              "4            1            4         1.0           0.0   \n",
              "\n",
              "   Diabetic_No, borderline diabetes  Diabetic_Yes (during pregnancy)  \\\n",
              "0                               0.0                              0.0   \n",
              "1                               0.0                              0.0   \n",
              "2                               0.0                              0.0   \n",
              "3                               0.0                              0.0   \n",
              "4                               0.0                              0.0   \n",
              "\n",
              "   GenHealth  SleepTime  Asthma  KidneyDisease  SkinCancer  BMI  Is_Male  \n",
              "0          3      0.250       1              0           1    0        0  \n",
              "1          3      0.500       0              0           0    1        0  \n",
              "2          1      0.625       1              0           0    2        1  \n",
              "3          2      0.375       0              0           1    1        0  \n",
              "4          3      0.625       0              0           0    1        0  "
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <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>HeartDisease</th>\n",
              "      <th>Smoking</th>\n",
              "      <th>AlcoholDrinking</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>PhysicalHealth</th>\n",
              "      <th>DiffWalking</th>\n",
              "      <th>AgeCategory</th>\n",
              "      <th>Race_White</th>\n",
              "      <th>Diabetic_Yes</th>\n",
              "      <th>Diabetic_No, borderline diabetes</th>\n",
              "      <th>Diabetic_Yes (during pregnancy)</th>\n",
              "      <th>GenHealth</th>\n",
              "      <th>SleepTime</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>KidneyDisease</th>\n",
              "      <th>SkinCancer</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Is_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.250</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.674342</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.625</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>11</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.375</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.674342</td>\n",
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              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.625</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "t_df_imp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "6752b638",
      "metadata": {
        "scrolled": false,
        "id": "6752b638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "2b728e9e-83ef-4977-ce89-972200e7553f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          variables       VIF\n",
              "0       AgeCategory  4.461473\n",
              "11        GenHealth  4.435149\n",
              "9        Race_White  4.320069\n",
              "7               BMI  3.694353\n",
              "6           Is_Male  1.891697\n",
              "4           Smoking  1.769483\n",
              "2      Diabetic_Yes  1.329578\n",
              "5        SkinCancer  1.198432\n",
              "8            Asthma  1.163536\n",
              "10  AlcoholDrinking  1.095492\n",
              "3     KidneyDisease  1.090319\n",
              "1            Stroke  1.084463"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ea9d39d-3ae2-4e30-a667-f32a6fcc0b57\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variables</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AgeCategory</td>\n",
              "      <td>4.461473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GenHealth</td>\n",
              "      <td>4.435149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Race_White</td>\n",
              "      <td>4.320069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BMI</td>\n",
              "      <td>3.694353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Is_Male</td>\n",
              "      <td>1.891697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Smoking</td>\n",
              "      <td>1.769483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diabetic_Yes</td>\n",
              "      <td>1.329578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SkinCancer</td>\n",
              "      <td>1.198432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Asthma</td>\n",
              "      <td>1.163536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>AlcoholDrinking</td>\n",
              "      <td>1.095492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KidneyDisease</td>\n",
              "      <td>1.090319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Stroke</td>\n",
              "      <td>1.084463</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "imp_feats = ['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', \n",
        "             'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']\n",
        "\n",
        "calc_vif(t_df_imp[imp_feats])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34f20baa",
      "metadata": {
        "id": "34f20baa"
      },
      "source": [
        "#### Note: All the features VIF scores are under 5, so we do not have any multi collinear features."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d2231f4",
      "metadata": {
        "id": "8d2231f4"
      },
      "source": [
        "### Which predictor variables are the most important?\n",
        "* `['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'is_Male', 'BMI', 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']` these features are independent of each other and have significance importance in identifying HeartDisease."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "521885f5",
      "metadata": {
        "id": "521885f5"
      },
      "source": [
        "# <b>5 <span style='color:#0386f7de'>|</span> Model Building</b>\n",
        "\n",
        "* We have cleaned, proceesed, Transformed our Data and found important features to predict Heart Disease. We are in the final stage, lets build a model and check the model performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "fd8f42cf",
      "metadata": {
        "scrolled": true,
        "id": "fd8f42cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69da05cd-a080-4251-f444-1146df6597b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking the stratified split...\n",
            "\n",
            "\n",
            "Target proportion in original dataset:\n",
            "No     91.440454\n",
            "Yes     8.559546\n",
            "Name: HeartDisease, dtype: float64\n",
            "\n",
            "Target proportion in Train dataset:\n",
            "0    91.440341\n",
            "1     8.559659\n",
            "Name: HeartDisease, dtype: float64\n",
            "\n",
            "Target proportion in Test dataset:\n",
            "0    91.440794\n",
            "1     8.559206\n",
            "Name: HeartDisease, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Using Startified Sampling to split into test, train sets\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "selected_feats = ['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', \n",
        "             'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']\n",
        "final_df = t_df_imp.copy()\n",
        "\n",
        "\n",
        "X = final_df[selected_feats].copy()\n",
        "y = final_df['HeartDisease'].copy()\n",
        "\n",
        "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.25, random_state=42)\n",
        "    \n",
        "for train_index, test_index in sss.split(X, y):\n",
        "    X_train, X_test = X.loc[train_index], X.loc[test_index]\n",
        "    y_train, y_test = y.loc[train_index], y.loc[test_index]\n",
        "\n",
        "print('Checking the stratified split...\\n')\n",
        "print('\\nTarget proportion in original dataset:')\n",
        "print(100*df['HeartDisease'].value_counts(normalize=True))\n",
        "\n",
        "print('\\nTarget proportion in Train dataset:')\n",
        "print(100*y_train.value_counts(normalize=True))\n",
        "\n",
        "print('\\nTarget proportion in Test dataset:')\n",
        "print(100*y_test.value_counts(normalize=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "563331b2",
      "metadata": {
        "id": "563331b2"
      },
      "source": [
        "#### Stratified Sampling is a sampling method that reduces the sampling error and it allows to create a test set with a population that best represents the entire population being studied.\n",
        "* As we can see that Tain and Test sets have same Target distribution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "e62644f5",
      "metadata": {
        "id": "e62644f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7756749-f208-480a-b258-15abb3287f93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model: LogisticRegression(random_state=0)\n",
            "Accuracy_score: 0.9161465434214312\n",
            "Precission_score: 0.5516728624535316\n",
            "Recall_score: 0.10843197428028642\n",
            "F1-score: 0.18124084025403028\n"
          ]
        }
      ],
      "source": [
        "lr = LogisticRegression(random_state=0)\n",
        "lr.fit(X_train, y_train)\n",
        "lr_y_predict = lr.predict(X_test)\n",
        "\n",
        "print(f'model: {str(lr)}')\n",
        "print(f'Accuracy_score: {accuracy_score(y_test,lr_y_predict)}')\n",
        "print(f'Precission_score: {precision_score(y_test,lr_y_predict)}')\n",
        "print(f'Recall_score: {recall_score(y_test,lr_y_predict)}')\n",
        "print(f'F1-score: {f1_score(y_test,lr_y_predict)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d561e535",
      "metadata": {
        "id": "d561e535"
      },
      "source": [
        "### Accuracy of the current model is very good but the precision and Recall are very bad, as our data is unbalanced. Let's do oversampling and build another model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "c49949fb",
      "metadata": {
        "scrolled": false,
        "id": "c49949fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "7145bef5-512e-45d9-e22b-70e9af8df3e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HeartDisease  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
              "0             0        1                0       0        0.600000   \n",
              "1             0        0                0       1        0.000000   \n",
              "2             0        1                0       0        0.674342   \n",
              "3             0        0                0       0        0.000000   \n",
              "4             0        0                0       0        0.674342   \n",
              "\n",
              "   DiffWalking  AgeCategory  Race_White  Diabetic_Yes  \\\n",
              "0            0            7         1.0           1.0   \n",
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              "2            0            9         1.0           1.0   \n",
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              "4            1            4         1.0           0.0   \n",
              "\n",
              "   Diabetic_No, borderline diabetes  Diabetic_Yes (during pregnancy)  \\\n",
              "0                               0.0                              0.0   \n",
              "1                               0.0                              0.0   \n",
              "2                               0.0                              0.0   \n",
              "3                               0.0                              0.0   \n",
              "4                               0.0                              0.0   \n",
              "\n",
              "   GenHealth  SleepTime  Asthma  KidneyDisease  SkinCancer  BMI  Is_Male  \n",
              "0          3      0.250       1              0           1    0        0  \n",
              "1          3      0.500       0              0           0    1        0  \n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24bcf65e-7dba-40b4-8deb-41f75e600728 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24bcf65e-7dba-40b4-8deb-41f75e600728');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "final_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67db6aad",
      "metadata": {
        "id": "67db6aad"
      },
      "source": [
        "### OverSampling: Balancing the each class distribution to build a robust model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "7fccde9c",
      "metadata": {
        "id": "7fccde9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79fe3c9-57c6-4ad1-f14d-9aba2c5ba815"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OverSampled Data Distribution:\n",
            " 0    292422\n",
            "1    292422\n",
            "Name: HeartDisease, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# OverSampling our Target and creating a new dataframe\n",
        "\n",
        "class_0 = t_df_imp[t_df_imp['HeartDisease'] == 0]\n",
        "class_1 = t_df_imp[t_df_imp['HeartDisease'] == 1]\n",
        "\n",
        "class_1 = class_1.sample(len(class_0),replace=True)\n",
        "balanced_df = pd.concat([class_0, class_1], axis=0)\n",
        "print('OverSampled Data Distribution:\\n',balanced_df['HeartDisease'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "c2a2af09",
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        "id": "c2a2af09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7637ac7a-46e9-457e-a9ae-319bd98ac593"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AgeCategory  Stroke  Diabetic_Yes  KidneyDisease  Smoking  SkinCancer  \\\n",
              "0            7       0           1.0              0        1           1   \n",
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              "\n",
              "   Is_Male  BMI  Asthma  Race_White  AlcoholDrinking  GenHealth  \n",
              "0        0    0       1         1.0                0          3  \n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d34ff166-6d04-4f73-bf6e-ab1586ea5bf7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d34ff166-6d04-4f73-bf6e-ab1586ea5bf7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "balanced_df[['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', \n",
        "                 'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "f7a60739",
      "metadata": {
        "scrolled": true,
        "id": "f7a60739",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15b12eae-67ef-4b6c-d725-82e75fdf914f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking the Data Distribution by stratified split...\n",
            "\n",
            "Target proportion in original dataset:\n",
            "0    50.0\n",
            "1    50.0\n",
            "Name: HeartDisease, dtype: float64\n",
            "\n",
            "Target proportion in Train dataset:\n",
            "1    50.10695\n",
            "0    49.89305\n",
            "Name: HeartDisease, dtype: float64\n",
            "\n",
            "Target proportion in Test dataset:\n",
            "0    50.424613\n",
            "1    49.575387\n",
            "Name: HeartDisease, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Lets Split the data using Stratification \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "selected_feats = ['AgeCategory', 'Stroke', 'Diabetic_Yes', 'KidneyDisease', 'Smoking', 'SkinCancer', 'Is_Male', 'BMI', \n",
        "             'Asthma', 'Race_White', 'AlcoholDrinking', 'GenHealth']\n",
        "\n",
        "X = balanced_df[selected_feats].copy()\n",
        "y = balanced_df['HeartDisease'].copy()\n",
        "\n",
        "# Creating Test(75%), Train(15%), Validation(10%) sets\n",
        "X_t, X_test, y_t, y_test = train_test_split(X, y, test_size=0.15)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_t, y_t,  test_size=0.10)\n",
        "\n",
        "print('Checking the Data Distribution by stratified split...')\n",
        "print('\\nTarget proportion in original dataset:')\n",
        "print(100*balanced_df['HeartDisease'].value_counts(normalize=True))\n",
        "\n",
        "print('\\nTarget proportion in Train dataset:')\n",
        "print(100*y_train.value_counts(normalize=True))\n",
        "\n",
        "print('\\nTarget proportion in Test dataset:')\n",
        "print(100*y_test.value_counts(normalize=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "ce9d412d",
      "metadata": {
        "scrolled": true,
        "id": "ce9d412d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0f80af40-5e67-4059-f1bb-fa6b90645863"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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4+/ubDbcGBARUeW8gERFVzdP7Idwu0aNYU2btVODuIoaHm9TaaRDRA66kTItSjb7Rz2trPwO3bduGli1bNkjbBw4cwOnTp/H888+bbH/ppZfs8nEXdlfo/fnnnwCAt99+G48//jjS09ORn5+PVatWoaysDKtXrwZwZ7GWyvvw7iaXy6FUKoXXKpUKcrncLM7Ly0uIq/z617jK15X773VOLy+vKqegWqJyuVVrU6vVJl+JasI+Q5bSaDTQ6o04eeoK9Abr5uIiFaN7cHM4ofE/XFHt8ecMWcKW+0t5eTkMBgMqKiqElR4rlai1+Cm3AOXainscXf9kUmf0CG4OV1nDrjR5LwaDQfhaeT1CQkIAwOz63K1y393H1YbRaITRaDQ7xs/PD82aNatyX0OoqKiAwWCAWq0WrsFf86zNfXx2V+hVvll/f3+8/fbbAICIiAiIxWLMnz8fiYmJ8PPzs2aKDUKn0yE3N9faaQjy8/OtnQLZGfYZqi2xWAyJWxMU3LyFUrX5CsiNyd1VivYtZfjz2m3o9Sz2bB1/zpAlbLW/iMVilJeXm2wTiUSoqKhAaVk51OWN97NIrxcLz5CraoHEe/nXv/6FJUuWYN++fSaPiVAqlRg8eDD+/ve/45FHHsH777+PM2fOoKSkBG3atMELL7xgsuJ+5e1RWq0WGo0GANCtWzfMnDkTEyZMEOLee+89bNu2DWVlZejVq5ewPsfdx3388cf45ptv8Mcff0AikaBz586YPXs22rZtCwB48803sXv3bgBAp06dAABPPvkkFi9ejDfffBNnzpzBjh07hHOeO3cOa9aswYkTJyAWixEeHo5Zs2aZPPuuW7dumDFjBjQaDT7//HMYDAb06dMHSUlJwmMUqlJeXg69Xl/tjECptOZRVrsr9CqnYv518ZVevXoBuHPR/fz8IJfLzVbhBO6Mut09nVMul6O4uNgsTqlUCnGVX4uLi+Hr62vS1t375XI5rl+/Xm1bdSWRSNC+ffv7aqM+qNVq5Ofno127dtV2UKJK7DNkKY1Gg+u3SiCXy+HmYd2V51xlYnh5eaNJq4esmgdVjz9nyBK23F/Ky8tx9epVyGQyuLi4mOxzVhsgFoshbrwBPYjFYjg7O0Mmk1l03NChQ7Fs2TL88MMPJtMgKwup4cOH4+jRo+jRowfGjRsHmUyGnJwcvPXWWxCLxXj66acB/K+YkUqlJtdDLBYLr7du3YqNGzdi8uTJiIiIwNGjR7FkyRKz427duoUXXngBLVu2RElJCbZt24bJkycjMzMT3t7emDp1KpRKJfLy8rBixQoAQJMmTeDi4iLcNy6TySASiXDt2jXEx8fDz88PK1asQHl5Od555x1MmTIFX375pXArGQBs374d3bt3R3JyMvLz87Fy5Uo0b94cs2bNqvHat2nTpsprf/78+Vp9H+yu0PPz86u2gq38DUhAQAD+/PNPsyJLoVAgICBAeF3V/XPFxcW4efOmEFf59a/HKhQKSCQSYQQxICAA2dnZZsOpeXl56NChQ13fMoA7v8mxpWequLq62lQ+ZPvYZ8gyJZBIJBAZrFvoicXOEIvF7Lt2gj9nyBK22F+cnJzg5OQEZ2dnswdli0ROEIlEjbpYlUgkgkjkZPFDu729vdG3b19kZmaajLxlZmYiKioKTZs2xZNPPilsNxqNCA8Px40bN7Bjxw5h/YzK91p5TSpVvq6oqEB6ejpGjBiBpKQkAEDfvn1x+/ZtfPXVVybHvfHGG8LxFRUV6NOnDyIiInDgwAGMGTMG/v7+aNq0Ka5du4Zu3bqZXYfKr87Ozvj444+h1+uxefNmYUHHzp07Y9iwYfjqq68wfvx44dhmzZoJt5YBwG+//Yb9+/fjtddeu+f1c3Z2hpOTE1xdXc0K/rvzqYndLWsmlUoRGRmJ7Oxsk+1Hjx4FcOciA0BUVBScnJywf/9+IUapVOLw4cOIjo4WtkVHR+Po0aPC6BwA7Nu3D05OTsLqmX5+fmjXrh327dtncs7MzExEREQIhWd0dDSUSqVJbnl5eThz5ozJOYmIiIiIHNmwYcNw4sQJXL16FQBw48YN/N///R+GDRsG4M7n8qVLl6J///7o3LkzOnfujG3btiEvL6/W57h+/Tpu3LiBwYMHm2wfMmSIWeyJEycwefJkhIeHo1OnTujatSvKysrqNIX3p59+Qnh4uMmq/YGBgejYsSN+/vlnk9jevXubvA4MDKxyBmBDsLkRPbVajUOHDgEArly5gpKSEqHA6tmzJ3x8fDBt2jSMHTsWs2fPxsiRI3Hx4kWsWrUKTz75JNq0aQMAePjhhzFq1CisWLECTk5OaN68OVJTU+Hp6YmxY8cK5xs7diw+/vhjTJ06FQkJCSgoKMCKFSswduxY4Rl6ADB9+nTMmTMHbdq0QXh4ODIzM3Hy5Els2bJFiAkLC0NUVBTmzZuHuXPnQiaTYc2aNQgKCsLjjz/eGJePiIiIiMjq+vfvD1dXV+zZswfx8fHYu3cvZDKZyfOyc3JyMHXqVLRv3x4eHh749NNPsXfv3lqf4+bNmwAAHx8fk+0PPWQ63f7q1auIjY3Fo48+isWLF6NZs2aQSCRISEgwux+yNlQqFYKDg822N23a1GTRR8B8MUeJRCLce9jQbK7Qu3XrFl599VWTbZWvP/roI4SHh+PRRx9Feno6Vq5ciZdffhleXl4YM2YMEhMTTY6bP38+3N3dsWrVKpSWlqJbt254//33TVbG9PLywocffoglS5Zg6tSpcHd3x6hRo8zaGj58ONRqNdLT05GWlgZ/f3+sX78eYWFhJnFr165FcnIyFi5cCL1ej6ioKMyfPx9isc1daiIiIiKiBuHi4oJBgwYhMzMT8fHxyMzMRP/+/eHm5oby8nL88MMPSEpKMpnm+Mknn1h0jsq1MwoLC022V67SX+nf//43ysrKsH79eqHw0uv1ZkVZbXl5eeHWrVtm22/duoV27drVqc2GYHPVR+vWrfH777/XGBcREYGdO3dWGyOVSjF37lzMnTu32rjAwEB88MEHNZ5z9OjRNT4s0dPTE8uWLcOyZctqbI+IiIiIyFENHz4cU6ZMwb///W+cOHEC8fHxAO6shmkwGCCRSITYkpISfPfddxa1//DDD8PX1xfffvutyfTNb775xiROo9FAJBKZDLzs3bvXbDVliURSqxG+7t27Y/v27SZrgSgUCvz+++9mz+e2Jpsr9IiIiIiIyP717t0b3t7emDdvHuRyubBmhaenJ0JCQpCeng4fHx+IxWKkpaXBw8PDbHSuOs7OzpgyZQr+8Y9/oGnTpoiMjMSRI0dw7Ngxk7jK1flff/11jB07FufOncP7779vNq0yMDAQO3fuxNdff422bduiSZMmaN26tdl5J02ahF27diE2NhYvv/wyysvLsXbtWrRo0QIjR4609DI1GLtbjIWIiIiI6EHkIhXDRerciH/ub0xIIpFgyJAhuHHjBh5//HGTlfNXrVqFNm3aICkpCUuXLsWQIUOExypYYvz48Zg+fTq++uorTJs2Dfn5+Vi6dKlJTFBQEJKTk/Hrr78iISEBe/bswTvvvGNyOxcAjBo1Ck888QSWLFmCUaNGYf369VWes0WLFvj444/h5eWFOXPmYMGCBejYsSM+/vhjeHh4WPweGorIaMnTD8kqTp06BQAICQmxciZAWVkZcnNzERwcbHNLEpNtYp8hS5WVlSH/yp84nV8KvZUfr+AidUZk11Zo7sO+a8v4c4YsYcv9RaPRIC8vD/7+/mbL6peUaVGqabyHpVdydxHDw63mh3M7soqKCmg0Gri4uFj8qIm6qK4fALWvDTh1k4iIiIjIxnm4SR/4gossw6mbREREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERER1asDBw5g69at9d6uSqVCSkoKzp8/X+9tOxqxtRMgIiIiIqLqlZRpUarRN/p53V3E8HCTWnzcgQMHcPr0aTz//PP1mo9KpcL69evxyCOPoH379vXatqNhoUdEREREZONKNXr8nFsAjbbxij0XqRjdg5vXqdAj62OhR0RERERkBzRaPTTaCmunUaOkpCR88cUXAICgoCAAwMiRI7F8+XLk5ORgzZo1OHnyJJydndGvXz/MmzcPTZs2FY5PS0vDjh07cP36dbi7u6Njx45YsmQJRCIRBg4cCAB49dVXhfiDBw+idevWjfgO7QMLPSIiIiIiqjevvPIKCgsLoVAosHLlSgCAj48PcnJyMH78ePTt2xdr1qyBWq3G2rVr8corr2Dbtm0AgC+//BLvvPMOZsyYgdDQUBQXF+Pnn39GaWkpAgICsH79ekybNg2zZs1CeHg4AKBZs2ZWe6+2jIUeERERERHVmzZt2sDHxwdXr15FaGiosH3evHl49NFHsX79eohEIgBAhw4dMHz4cBw6dAh9+/bFyZMnERQUhISEBOG4QYMGCX8PDg4GALRt29akbTLHVTeJiIiIiKhBqdVqHD9+HE888QQqKiqg1+uh1+vRrl07tGjRAqdOnQIAdOrUCWfOnEFycjJ++ukn6HQ6K2duvziiR0REREREDUqlUqGiogLJyclITk4223/t2jUAwN/+9jeUlpZi+/bt+OCDD+Dp6Ymnn34ac+bMgYuLS2OnbddY6BERERERUYPy9PSESCRCQkKCyVTMSk2aNAEAODk5YeLEiZg4cSIKCgqwZ88erFq1Ck2aNMHUqVMbO227xkKPiIiIiIjqlUQiQXl5ufDazc0NoaGhUCgUCAkJqVUbzZs3R2xsLL7++msoFAqhXQAmbVPVbO4evYsXL2LhwoUYMWIEOnXqhOHDh1cbf+DAAQQFBVUZV1xcjHnz5qFnz54ICwvDjBkzcOPGDbO448ePY8yYMejSpQv69++PtLQ0GI1Gkxij0Yi0tDT069cPXbp0wZgxY3DixAmztgoKCjB9+nSEhYWhZ8+eeOONN1BSUmLZRSAiIiIismOBgYG4cuUKvv76a5w6dQqXL1/G3//+d/zwww+YOXMmvv32Wxw7dgxfffUV5s6di2PHjgEAFi5ciJUrV+LAgQP48ccfsWnTJvz222/o1asXAMDX1xdyuRx79uzBzz//jFOnTkGr1VrzrdosmxvRO3fuHA4dOoSuXbvCYDCYFVx302g0WLZsGR566KEq98+cORPnz5/HokWLIJPJsHbtWsTHx2Pnzp0Qi++89YsXLyIuLg6RkZGYOXMmfv/9d6xcuRLOzs6Ii4sT2kpPT8e6deswZ84cBAUFYevWrYiNjcVXX30FPz8/AIBOp8OLL74IAFi1ahU0Gg3efvttzJ49G6mpqfV1iYiIiIjoAeQibdyP7vdzvlGjRuHkyZNYsmQJioqKhOfoffLJJ0hJScHrr78OnU6Hhx9+GL169ULbtm0BAGFhYdi+fTt27NgBtVoNPz8/vP766xg9ejSAO1M7k5OTsXr1akyaNAlarZbP0bsHmyv0BgwYIMzbTUpKwunTp+8Zm5qaipYtW6J169ZmcTk5OTh8+DAyMjIQFRUFAPD390dMTAz279+PmJgYAEBGRgaaNGmC1atXQyqVIiIiAoWFhdi0aRPGjx8PqVSK8vJypKamIjY2FpMmTQIAdO/eHU888QQyMjKwaNEiAMA333yDc+fOITMzEwEBAQAAuVyOuLg4nDx5El26dKnPS0VEREREDwh3FzG6Bze3ynnrwsPDA6tXrzbbHhISgrS0tHseN3LkSIwcObLatgcNGlTlfX5kyuYKPSen2s0m/eOPP/D+++/js88+wwcffGC2PysrC3K5HJGRkcK2gIAABAcHIysrSyj0srKyMHjwYEilUiEuJiYGqampyMnJQXh4OI4fP46SkhIMHTpUiJFKpRg8eDC+/fZbk3MGBQUJRR4AREZGwtvbG4cOHWKhR0RERER14uEmhYebtOZAov+yuXv0ausf//gHRowYgY4dO1a5X6FQwN/fX3gYY6WAgADhZs6ysjJcu3bNpDCrjBGJREJc5de/xgUGBuLq1avQaDRC3F9jRCIR/P39hTaIiIiIiIgams2N6NXGd999h5ycHOzbt++eMSqVCp6enmbbvby8hGmexcXFAO5Mr7ybVCqFq6srlEql0JZUKoVMJjOJk8vlMBqNUCqVcHFxqfaclW3VldFoRFlZ2X21UR/UarXJV6KasM+QpSp/eabT6aA3iGqIblhiJyP0er1N/Pyle+PPGbKELfeX8vJyGAwGVFRUoKKiwtrp0H9VrhliNBob5ftSUVEBg8EAtVoNg8FQZT5/Hcyqit0VeuXl5Vi2bBmmT58OHx8fa6fTaHQ6HXJzc62dhiA/P9/aKZCdYZ+h2hKLxZC4NYFKpUKp2rorqbm7SqFUuuPPa7eh1+utmgvVjD9nyBK22l/EYjEfHWCjGuv7Ul5eDr1eX+2MwLtvO7sXuyv0PvzwQzg5OWHYsGFQqVQA7hRBBoMBKpUKLi4ukEqlkMvluH79utnxSqUSXl5eACCMvlWO7FXSarVQq9VCnFwuh1arRXl5ucmonkqlgkgkMomr6lEKSqUSLVq0uK/3LZFI0L59+/tqoz6o1Wrk5+ejXbt2cHV1tXY6ZAfYZ8hSGo0G12+VQC6Xw83DuiN6rjIxvLy80aRV1as7k23gzxmyhC33l/Lycly9ehUymQwuLi7WTof+y2g0CnVAbUbS6oNYLEabNm3MZhQCwPnz52vXRn0n1dAUCgUuXryIiIgIs32PPfYYFi1ahHHjxiEgIADZ2dlmQ5t5eXno0KEDgDsPbmzRooVZtZyXlwej0Sjcb1f5NS8vz+SeQIVCgZYtWwr/EAMCAnD27FmTtoxGI/Ly8kwWhakLkUgENze3+2qjPrm6utpUPmT72GfIMiWQSCQQWXvqptgZYrGYfddO8OcMWcIW+4uTkxOcnJzg7OwMZ2dna6dD/1U5XVMkEjXK98XZ2RlOTk5wdXWtsuCvbbFpd4uxxMfH46OPPjL5ExUVhVatWuGjjz7CgAEDAADR0dFQKpXIzs4Wjs3Ly8OZM2cQHR0tbIuOjsbBgweh0+mEbZmZmZDL5QgLCwMAdOvWDR4eHti7d68Qo9PpsH//frO2fvvtN5OpANnZ2SgqKkLfvn3r/VoQERERERFVxeZG9NRqNQ4dOgQAuHLlCkpKSoRFV3r27InAwEAEBgaaHPPFF1+goKAA4eHhwrawsDBERUVh3rx5mDt3LmQyGdasWYOgoCA8/vjjQlxcXBx2796N2bNnY9y4cTh79iwyMjKQmJgozH2VyWRISEhASkoKfHx80KFDB3z66acoKioyeaj6kCFDkJqaiunTp2PWrFlQq9VYsWIF+vXrx0crEBERERFRo7G5Qu/WrVt49dVXTbZVvv7oo49MirmarF27FsnJyVi4cCH0ej2ioqIwf/58iMX/e9tt27ZFRkYGli9fjilTpsDHxwczZsxAbGysSVvx8fEwGo3YvHkzCgsLERwcjIyMDPj5+QkxEokE7733HpYuXYpZs2ZBLBZj8ODBmDdvXl0uBRERERERUZ2IjJXrhZLNOnXqFAAgJCTEypncefZgbm4ugoODbW5eO9km9hmyVFlZGfKv/InT+aVWf7yCi9QZkV1bobkP+64t488ZsoQt9xeNRoO8vDz4+/vb/WIsBw4cQEFBAZ5//vl6a3PAgAHo168fFi5cWG9t1kZFRQU0Gg1cXFwa5R69mvpBbWsDmxvRIyIiIiIi+3bgwAGcPn26Xgu99evXmz3/mu6NhR4RERERkY0rKdOiVNP4z/N0dxHDw63mZ7bVhdFohE6nq9Uz4QCgU6dODZKHo2KhR0RERERk40o1evycWwCNtvGKPRepGN2Dm1tc6CUlJeGLL74AAAQFBQEARo4cCQA4ffo0XnvtNaxatQoKhQIrV65EdHQ0Vq5ciSNHjuD69eto2rQpoqKi8NprrwnPvQbMp24mJSXh9OnTWLBgAZKTk5Gfn4/27dtj0aJFePTRR+vjEtg1FnpERERERHZAo9VDo62wdho1euWVV1BYWCgUcgDg4+ODjRs34saNG1i6dClefvlltGjRAi1btoRGo0FFRQUSExPh4+ODa9euYdOmTXjllVfw8ccfV3uumzdvYunSpZgyZQo8PT2xatUqTJs2Dd9++y0kEkljvF2bxUKPiIiIiIjqTZs2beDj44OrV68iNDTUZJ9SqUR6ejq6du1qsn3x4sXC3/V6PVq3bo3nnntOWJTkXpRKJbZs2YJHHnkEAODq6ooJEybgl19+QY8ePervTdkhFnpERERERNQovL29zYo8APjyyy/xwQcf4OLFiygrKxO25+fnV1voNWvWTCjyAKB9+/YAgIKCgnrM2j6x0CMiIiIiokbx0EMPmW379ttvMXfuXIwZMwaJiYnw9vbGzZs3MXXqVJSXl1fb3l9X4aycrlnTcQ8CFnpERERERNQoRCLz56Pu27cPwcHBeOutt4RtP/74Y2Om5ZCcrJ0AERERERE5FolEUutRNY1GY7Zwyu7duxsirQcKCz0iIiIiIqpXgYGBuHLlCr7++mucOnUKly9fvmds7969cfLkSWzYsAFHjx5FcnIysrOzGzFbx8Spm0REREREdsBF2rgf3e/nfKNGjcLJkyexZMkSFBUVCc/Rq8rYsWNx+fJlbNmyBRkZGYiKisKqVavw7LPP1vn8xEKPiIiIiMjmubvceXi5Nc5bFx4eHli9enWtYp2dnTF37lzMnTvXZPvvv/9u8vq7774zeb18+XKztuRyudlxDyoWekRERERENs7DTQoPN6m10yA7wnv0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNAjIiIiIiJyMCz0iIiIiIhsiNFotHYKZEX19f1noUdEREREZAMkEgkAoKyszMqZkDVVfv8r+0Nd8fEKREREREQ2wNnZGd7e3rhx4wYAwM3NDSKRyMpZUUVFBcrLywHc+R41FKPRiLKyMty4cQPe3t73fS4WekRERERENuLhhx8GAKHYI+szGAzQ6/UQi8Vwcmr4CZHe3t5CP7gfLPSIiIiIiGyESCRCixYt0KxZM+h0OmunQwDUajUUCgXatGkDV1fXBj2XRCKpt1FDFnpERERERDbG2dm5QacJUu0ZDAYAgEwmg4uLi5WzqT0uxkJERERERORgbK7Qu3jxIhYuXIgRI0agU6dOGD58uMn+kpISpKSkYNSoUejRowd69+6Nl156Cb///rtZW8XFxZg3bx569uyJsLAwzJgxo8r5zsePH8eYMWPQpUsX9O/fH2lpaWbLmhqNRqSlpaFfv37o0qULxowZgxMnTpi1VVBQgOnTpyMsLAw9e/bEG2+8gZKSkvu7KERERERERBawuULv3LlzOHToENq2bYvAwECz/VevXsW2bdsQGRmJtWvXYsmSJSguLsaYMWNw4cIFk9iZM2fiyJEjWLRoEVauXIm8vDzEx8dDr9cLMRcvXkRcXBx8fX2RmpqKiRMnYt26ddi8ebNJW+np6Vi3bh0mTZqE1NRU+Pr6IjY2FpcuXRJidDodXnzxReTn52PVqlVYtGgRDh8+jNmzZ9fzVSKyL41x4zIRERER/Y/N3aM3YMAADBo0CACQlJSE06dPm+xv3bo1vv32W5MbIXv16oUBAwbgk08+wYIFCwAAOTk5OHz4MDIyMhAVFQUA8Pf3R0xMDPbv34+YmBgAQEZGBpo0aYLVq1dDKpUiIiIChYWF2LRpE8aPHw+pVIry8nKkpqYiNjYWkyZNAgB0794dTzzxBDIyMrBo0SIAwDfffINz584hMzMTAQEBAAC5XI64uDicPHkSXbp0abDrRmTLPL0fwu0SPYo11n8ukLuLGB5uUmunQURERNSgbK7Qq+k3/25ubmbb3N3d0aZNG5NpmVlZWZDL5YiMjBS2BQQEIDg4GFlZWUKhl5WVhcGDB0Mq/d8Hv5iYGKSmpiInJwfh4eE4fvw4SkpKMHToUCFGKpVi8ODB+Pbbb03OGRQUJBR5ABAZGQlvb28cOnSIhR49sLR6I87kFkBvsG4eLlIxugc3Z6FHREREDs/mCr26UKlUOHfuHHr37i1sUygU8Pf3N3vIZEBAABQKBYA7T52/du2aSWFWGSMSiaBQKBAeHi7E/zUuMDAQH374ITQaDVxcXKBQKMxiRCIR/P39hTaIHlQarR56Ax/6SkRERNQYHKLQ++c//wmRSIRx48YJ21QqFTw9Pc1ivby8hOmgxcXFAO5Mr7ybVCqFq6srlEql0JZUKoVMJjOJk8vlMBqNUCqVcHFxqfaclW3VldFoRFmZ9ae9qdVqk69ENdFoNADu3MNq7UJP7GSEXq+3iX9LdG/sM2Qp/t9ElmB/IUvZWp8xGo1mg1lVsftCb+fOndi+fTuWL19eL0+Qt1U6nQ65ubnWTkOQn59v7RTITojFYkjcmkClUqFUrbVqLu6uUiiV7vjz2m2TRZnItrDPUF3x/yayBPsLWcqW+szdt53di10XeocOHcLChQvxyiuvYOTIkSb75HI5rl+/bnaMUqmEl5cXAAijb5Uje5W0Wi3UarUQJ5fLodVqUV5ebjKqp1KpIBKJTOKqepSCUqlEixYt7uOdAhKJBO3bt7+vNuqDWq1Gfn4+2rVrZ7IgDtG9aDQaXL9VArlcDjcP647OuMrE8PLyRpNWD1k1D6oe+wxZiv83kSXYX8hSttZnzp8/X6s4uy30Tpw4gVdffRVPP/00Xn31VbP9AQEByM7ONhvazMvLQ4cOHQDcWdilRYsWZvfP5eXlwWg0CvfbVX7Ny8tDx44dhTiFQoGWLVvCxcVFiDt79qxJW0ajEXl5eSaLwtSFSCSqciEaa3F1dbWpfMjWlUAikUBk7Wl4YmeIxWL2XbvAPkOW4/9NZAn2F7KUrfSZ2kzbBGzwOXq1cf78eSQkJKBXr15YvHhxlTHR0dFQKpXIzs4WtuXl5eHMmTOIjo42iTt48CB0Op2wLTMzE3K5HGFhYQCAbt26wcPDA3v37hVidDod9u/fb9bWb7/9ZjKsm52djaKiIvTt2/e+3zcREREREVFt2NyInlqtxqFDhwAAV65cQUlJCfbt2wcA6NmzJ4xGI+Li4iCTyTBx4kST5+x5eHgI0xvDwsIQFRWFefPmYe7cuZDJZFizZg2CgoLw+OOPC8fExcVh9+7dmD17NsaNG4ezZ88iIyMDiYmJwtxXmUyGhIQEpKSkwMfHBx06dMCnn36KoqIixMXFCW0NGTIEqampmD59OmbNmgW1Wo0VK1agX79+fLQCERERERE1Gpsr9G7dumU2FbPy9UcffQQAwr13lQ8vr9SzZ098/PHHwuu1a9ciOTkZCxcuhF6vR1RUFObPnw+x+H9vu23btsjIyMDy5csxZcoU+Pj4YMaMGYiNjTVpOz4+HkajEZs3b0ZhYSGCg4ORkZEBPz8/IUYikeC9997D0qVLMWvWLIjFYgwePBjz5s27/wtDRERERERUSzZX6LVu3Rq///57tTE17a/k6emJZcuWYdmyZdXGdevWDdu3b682RiQSISEhAQkJCdXGNW/eHCkpKbXKj4iIiIiIqCHY5T16REREREREdG8s9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHY3OF3sWLF7Fw4UKMGDECnTp1wvDhw6uM27FjB4YMGYKQkBA89dRT+P77781iiouLMW/ePPTs2RNhYWGYMWMGbty4YRZ3/PhxjBkzBl26dEH//v2RlpYGo9FoEmM0GpGWloZ+/fqhS5cuGDNmDE6cOGHWVkFBAaZPn46wsDD07NkTb7zxBkpKSup2MYiIiIiIiOrA5gq9c+fO4dChQ2jbti0CAwOrjNmzZw8WLFiAoUOHIj09HaGhoZg2bZpZ4TVz5kwcOXIEixYtwsqVK5GXl4f4+Hjo9Xoh5uLFi4iLi4Ovry9SU1MxceJErFu3Dps3bzZpKz09HevWrcOkSZOQmpoKX19fxMbG4tKlS0KMTqfDiy++iPz8fKxatQqLFi3C4cOHMXv27Pq7QERERERERDUQWzuBvxowYAAGDRoEAEhKSsLp06fNYtatW4dhw4Zh5syZAIBevXrh7Nmz2LBhA9LT0wEAOTk5OHz4MDIyMhAVFQUA8Pf3R0xMDPbv34+YmBgAQEZGBpo0aYLVq1dDKpUiIiIChYWF2LRpE8aPHw+pVIry8nKkpqYiNjYWkyZNAgB0794dTzzxBDIyMrBo0SIAwDfffINz584hMzMTAQEBAAC5XI64uDicPHkSXbp0aajLRkREREREJLC5ET0np+pTunTpEvLz8zF06FCT7TExMcjOzoZWqwUAZGVlQS6XIzIyUogJCAhAcHAwsrKyhG1ZWVkYOHAgpFKpSVsqlQo5OTkA7kztLCkpMTmnVCrF4MGDzdoKCgoSijwAiIyMhLe3Nw4dOmTJZSAiIiIiIqozmyv0aqJQKADcGZ27W2BgIHQ6nTCVUqFQwN/fHyKRyCQuICBAaKOsrAzXrl0zKcwqY0QikRBX+fWvcYGBgbh69So0Go0Q99cYkUgEf39/oQ0iIiIiIqKGZnNTN2uiVCoB3JkSebfK15X7VSoVPD09zY738vISpoMWFxdX2ZZUKoWrq6tJW1KpFDKZzOycRqMRSqUSLi4u1Z6zsq26MhqNKCsru6826oNarTb5SlSTyl+E6HQ66A2iGqIbltjJCL1ebxP/luje2GfIUvy/iSzB/kKWsrU+YzQazQazqmJ3hd6DSqfTITc319ppCPLz862dAtkJsVgMiVsTqFQqlKq1Vs3F3VUKpdIdf167bbIoE9kW9hmqK/7fRJZgfyFL2VKfufu2s3uxu0LPy8sLwJ3ROF9fX2G7SqUy2S+Xy3H9+nWz45VKpRBTOfpWObJXSavVQq1Wm7Sl1WpRXl5uMqqnUqkgEolM4qp6lIJSqUSLFi3q9ob/SyKRoH379vfVRn1Qq9XIz89Hu3bt4Orqau10yA5oNBpcv1UCuVwONw/rjs64ysTw8vJGk1YPWTUPqh77DFmK/zeRJdhfyFK21mfOnz9fq7g6F3r/93//h1atWqFly5b3jLl27RouX76Mxx57rK6nMVN5D9xf74dTKBSQSCTw8/MT4rKzs82GNvPy8tChQwcAgJubG1q0aGF2/1xeXh6MRqPQfuXXvLw8dOzY0eScLVu2hIuLixB39uxZk7aMRiPy8vJMFoWpC5FIBDc3t/tqoz65urraVD5k60ogkUggsvY0PLEzxGIx+65dYJ8hy/H/JrIE+wtZylb6TG2mbQL3sRjLhAkTsGvXrmpjvvzyS0yYMKGup6iSn58f2rVrh3379plsz8zMREREhDCMGR0dDaVSiezsbCEmLy8PZ86cQXR0tLAtOjoaBw8ehE6nM2lLLpcjLCwMANCtWzd4eHhg7969QoxOp8P+/fvN2vrtt99MhnWzs7NRVFSEvn371s8FICIiIiIiqkGdR/SMRmONMQaDodYVZyW1Wi08iuDKlSsoKSkRirqePXvCx8cH06dPx5w5c9CmTRuEh4cjMzMTJ0+exJYtW4R2wsLCEBUVhXnz5mHu3LmQyWRYs2YNgoKC8PjjjwtxcXFx2L17N2bPno1x48bh7NmzyMjIQGJiolA0ymQyJCQkICUlBT4+PujQoQM+/fRTFBUVIS4uTmhryJAhSE1NxfTp0zFr1iyo1WqsWLEC/fr14zP0iIiIiIio0TToPXoXL16schXK6ty6dQuvvvqqybbK1x999BHCw8MxfPhwqNVqpKenIy0tDf7+/li/fr0wAldp7dq1SE5OxsKFC6HX6xEVFYX58+dDLP7f227bti0yMjKwfPlyTJkyBT4+PpgxYwZiY2NN2oqPj4fRaMTmzZtRWFiI4OBgZGRkCFNFgTv30b333ntYunQpZs2aBbFYjMGDB2PevHkWXQMiIiIiIqL7YVGh9/rrr5u8PnjwIK5cuWIWZzAYcO3aNfz0008mUxtro3Xr1vj9999rjBs9ejRGjx5dbYynpyeWLVuGZcuWVRvXrVs3bN++vdoYkUiEhIQEJCQkVBvXvHlzpKSkVBtDRERERETUkCwq9L744gvh7yKRCLm5ufdc8l8kEiEkJMSsOCQiIiIiIqKGZVGhd/DgQQB37s8bNGgQJk6cWOViK87OzneWxbaBVWmIiIiIiIgeNBYVeq1atRL+npycjODgYJNtREREREREZH11Xoxl5MiR9ZkHERERERER1ZP7XnXz5MmTOHXqFFQqFSoqKsz2i0QiTJ069X5PQ0RERERERLVU50KvqKgIU6dOxfHjx6t9ph4LPSIiIiIiosZV50Jv+fLl+Pnnn9GzZ0+MHDkSDz/8MJydneszNyIiIiIiIqqDOhd633//Pbp06YIPP/wQIpGoPnMiIiIisoiTk5O1UyAisil1LvTKy8vRo0cPFnlERERkdZ7eD+F2iR7FmjJrpwJ3FzE83KTWToOIHnB1LvQ6duyIK1eu1GcuRERERHWi1RtxJrcAeoN183CRitE9uDkLPSKyujoXetOmTcPLL7+MEydOIDQ0tB5TIiIiIrKcRquH3sCZRkREwH0Uen/++Sf69euHF154AU8++SQ6d+4MDw+PKmOffvrpup6GiIiIiIiILFTnQi8pKQkikQhGoxFffPEFvvjiC7P79YxGI0QiEQs9IiIiIiKiRlTnQi85Obk+8yAiIiIiIqJ6UudCb+TIkfWZBxEREREREdUTPnSGiIiIiIjIwdR5RO/q1au1jm3ZsmVdT0NEREREREQWqnOhN2DAgFo9LF0kEuHMmTN1PQ0RERERERFZqM6F3tNPP11loVdcXIzffvsNly9fxmOPPYbWrVvfV4JERERERERkmToXesuXL7/nPqPRiM2bN+O9997DsmXL6noKIiIiIiIiqoMGWYxFJBIhLi4O7du3x4oVKxriFERERERERHQPDbrq5qOPPor//Oc/DXkKIiIiIiIi+osGLfQuXboEvV7fkKcgIiIiIiKiv6jzPXr3YjAYUFBQgF27duHgwYOIiIio71MQERERERFRNepc6HXs2LHaxysYjUZ4eXlh7ty5dT1FtQ4ePIhNmzbh/PnzcHd3R/fu3TFnzhz4+fmZxO3YsQPvvfcerl69Cn9/fyQmJqJ///4mMcXFxUhOTsaBAweg0+nQp08fzJ8/H82aNTOJO378ON5++23k5uaiadOmGDduHOLj402ug9FoRHp6Oj755BMUFhYiODgYr7/+OkJDQxvkOhAREREREf1VnQu9xx57rMrtTk5O8PLywqOPPopnnnkGTZs2rXNy93Ls2DFMmzYNTz/9NBITE1FUVIR33nkHsbGx2L17N1xcXAAAe/bswYIFC/DSSy+hV69eyMzMxLRp07B161aTwmvmzJk4f/48Fi1aBJlMhrVr1yI+Ph47d+6EWHznEl28eBFxcXGIjIzEzJkz8fvvv2PlypVwdnZGXFyc0FZ6ejrWrVuHOXPmICgoCFu3bkVsbCy++uorsyKUiIiIiIioIdS50Pv444/rMw+L7NmzBy1btsSyZcuE0TQfHx9MnDgRp0+fRo8ePQAA69atw7BhwzBz5kwAQK9evXD27Fls2LAB6enpAICcnBwcPnwYGRkZiIqKAgD4+/sjJiYG+/fvR0xMDAAgIyMDTZo0werVqyGVShEREYHCwkJs2rQJ48ePh1QqRXl5OVJTUxEbG4tJkyYBALp3744nnngCGRkZWLRoUeNdJCIiIiIiemA16GIsDUWv18Pd3d1kyqSnpyeAO1MngTsLweTn52Po0KEmx8bExCA7OxtarRYAkJWVBblcjsjISCEmICAAwcHByMrKErZlZWVh4MCBkEqlJm2pVCrk5OQAuDO1s6SkxOScUqkUgwcPNmmLiIiIiIioIdVLoffzzz9j69atSE1NxdatW/Hzzz/XR7P39Le//Q0XLlzA1q1bUVxcjEuXLmH16tXo1KkTunXrBgBQKBQA7ozO3S0wMBA6nQ6XLl0S4vz9/c3uNwwICBDaKCsrw7Vr1xAQEGAWIxKJhLjKr3+NCwwMxNWrV6HRaOrj7RMREREREVXrvlbdPH78OF5//XX88ccfAO6MplUWTG3btkVycjLCwsLuP8u/6NGjB9avX4/Zs2fjrbfeAgAEBwfjvffeg7OzMwBAqVQCAORyucmxla8r96tUKmE08G5eXl44ffo0gDuLtVTVllQqhaurq0lbUqkUMpnM7JxGoxFKpVK4f9BSRqMRZWVldTq2PqnVapOvRDWp/AWHTqeD3nDvBZwag9jJCL1ebxP/luje2GfIUuwzZAl+liFL2Vqfubvmqk6dC71z584hLi4OarUakZGRCA8Ph6+vL27evIljx47hyJEjiIuLw/bt29G+ffu6nqZKx48fx9///nc8++yz6NevH4qKirBx40ZMmTIFn3zySZ2LKVum0+mQm5tr7TQE+fn51k6B7IRYLIbErQlUKhVK1Vqr5uLuKoVS6Y4/r93mMz5tGPsMWYp9huqCn2XIUrbUZ+6+nexe6lzobdiwATqdDmlpaYiOjjbZN2XKFGRlZeGVV17Bhg0bsGbNmrqepkpLly5Fr169kJSUJGwLDQ1Fv3798NVXX2HMmDHw8vICcGc0ztfXV4hTqVQAIOyXy+W4fv262TmUSqUQUzniVzmyV0mr1UKtVpu0pdVqUV5ebjKqp1KpIBKJhLi6kEgk9V4w14VarUZ+fj7atWsHV1dXa6dDdkCj0eD6rRLI5XK4eVj3N+2uMjG8vLzRpNVDVs2Dqsc+Q5ZinyFL8LMMWcrW+sz58+drFVfnQu/HH3/EkCFDzIq8StHR0RgyZAiys7Preop7unDhAgYOHGiy7eGHH0aTJk2EaaSV98kpFAqTe+YUCgUkEonwqIOAgABkZ2ebDYHm5eWhQ4cOAAA3Nze0aNFCuAfv7hij0Si0X/k1Ly8PHTt2NDlny5Yt72ukUSQSwc3Nrc7H1zdXV1ebyodsXQkkEglE1p5SJXaGWCxm37UL7DNkKfYZsgw/y5ClbKXP1GbaJnAfi7EUFxejdevW1ca0bt3abBSsPrRs2RJnzpwx2XblyhXcvn0brVq1AgD4+fmhXbt22Ldvn0lcZmYmIiIihOHO6OhoKJVKk4I0Ly8PZ86cMSlio6OjcfDgQeh0OpO25HK5cB9it27d4OHhgb179woxOp0O+/fvv2dBTEREREREVN/qPKLXrFkznDhxotqYX375Bc2aNavrKe5p7NixWLZsGZYuXYoBAwagqKgI7777Lpo2bWryaIPp06djzpw5aNOmDcLDw5GZmYmTJ09iy5YtQkxYWBiioqIwb948zJ07FzKZDGvWrEFQUBAef/xxIS4uLg67d+/G7NmzMW7cOJw9exYZGRlITEwUikaZTIaEhASkpKTAx8cHHTp0wKeffoqioiKTh6oTERERERE1pDoXegMGDMCWLVuwdu1avPzyyyb3pFU+OPzYsWMYP358vSR6twkTJkAqleLTTz/Fzp074e7ujtDQUKxduxZNmjQR4oYPHw61Wo309HSkpaXB398f69evN1sJdO3atUhOTsbChQuh1+sRFRWF+fPnQyz+3+Vp27YtMjIysHz5ckyZMgU+Pj6YMWMGYmNjTdqKj4+H0WjE5s2bUVhYiODgYGRkZAhTRYmIiIiIiBqayFj5hHEL3b59G88++ywuX74Mb29vdOnSBU2bNsWtW7dw6tQpFBYWws/PDzt27IC3t3c9p/1gOXXqFAAgJCTEypnceaZgbm4ugoODbWKOMtm+srIy5F/5E6fzS62+7LmL1BmRXVuhuQ/7ri1jnyFLsc+QJfhZhixla32mtrVBnUf0mjRpgm3btuGf//wnMjMzcejQIWGfTCbD3/72N8yZM4dFHhERERERUSO7rwem+/j4IDk5GW+99RYUCgVKSkrg4eGBgIAASCSS+sqRiIiIiIiILGBxoffuu+9CrVZj+vTpQjEnkUgQFBQkxGi1WqxZswbu7u6YMmVK/WVLRERERERENbLo8QpHjx7FunXr4O3tXe2InVQqhbe3N9asWYP//Oc/950kERERERER1Z5Fhd6XX34JuVyOF154ocbY559/Hl5eXti1a1edkyMiIiIiIiLLWVTo5eTkoHfv3sJz46ojlUrRu3dvHD9+vM7JERERERERkeUsKvRu3Lhh0fPgWrdujZs3b1qcFBEREREREdWdRYWek5MTdDpdreN1Oh2cnCw6BREREREREd0ni6qwZs2a4dy5c7WOP3fuHJo1a2ZxUkRERERERFR3FhV63bt3x3/+8x9cvny5xtjLly/jP//5Dx577LE6J0dERERERESWs6jQe/7556HX6zFjxgwUFhbeM+727dt49dVXUVFRgXHjxt13kkRERERERFR7Fj0wvXPnzpg4cSI+/PBDDBs2DGPHjkV4eDgefvhhAEBBQQGys7Oxfft2FBYWYvLkyejcuXODJE5ERERERERVs6jQA4CkpCTIZDJkZGRg06ZN2LRpk8l+o9EIZ2dnJCQkYObMmfWVJxEREREREdWSxYWeSCTCrFmzMGrUKOzcuRM5OTn4888/AQAPPfQQunXrhr/97W9o06ZNvSdLRERERERENbO40KvUpk0bJCYm1mcuREREREREVA/4kDsiIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiByMXRd6X3zxBZ5++mmEhIQgPDwcL774IjQajbD/u+++w1NPPYWQkBAMGTIEO3fuNGtDq9Xi7bffRmRkJEJDQzF58mQoFAqzuAsXLmDy5MkIDQ1FZGQkVqxYAa1Waxa3Y8cODBkyBCEhIXjqqafw/fff1++bJiIiIiIiqoHdFnrvvvsulixZgpiYGGRkZOCtt95C69atUVFRAQD46aefMG3aNISGhiI9PR1Dhw7FG2+8gX379pm0s3TpUuzYsQOJiYlISUmBVqvFpEmTUFxcLMQolUpMnDgROp0OKSkpSExMxPbt27F8+XKTtvbs2YMFCxZg6NChSE9PR2hoKKZNm4YTJ040+PUgIiIiIiKqJLZ2AnWhUCiwfv16bNy4EX379hW2DxkyRPj7u+++iy5duuCtt94CAPTq1QuXLl3CunXr8MQTTwAArl+/js8//xxvvvkmRo0aBQAICQlB//798dlnnyE+Ph4A8Nlnn6G0tBTr16+Ht7c3AKCiogKLFy9GQkICmjdvDgBYt24dhg0bhpkzZwrnPHv2LDZs2ID09PQGvSZERERERESV7HJEb9euXWjdurVJkXc3rVaLY8eOCQVdpZiYGFy4cAGXL18GABw+fBgGg8EkztvbG5GRkcjKyhK2ZWVlISIiQijyAGDo0KEwGAw4cuQIAODSpUvIz8/H0KFDzc6ZnZ1d5TRPIiIiIiKihmCXhd4vv/yCDh06YOPGjYiIiMCjjz6KsWPH4pdffgEA/PHHH9DpdAgICDA5LjAwEACEe/AUCgWaNm0KLy8vs7i779NTKBRmbcnlcvj6+pq0BQD+/v5mbel0Oly6dOl+3zYREREREVGt2OXUzZs3b+L06dM4e/Ys3nzzTbi6umLTpk2IjY3F/v37oVQqAdwpxu5W+bpyv0qlgqenp1n7crlciKmM+2tbAODl5SXE1facdWU0GlFWVnZfbdQHtVpt8pWoJpULJOl0OugNIqvmInYyQq/X28S/Jbo39hmyFPsMWYKfZchSttZnjEYjRKKaf9bZZaFXWfS888476NixIwCga9euGDBgALZs2YKoqCgrZ1j/dDodcnNzrZ2GID8/39opkJ0Qi8WQuDWBSqVCqdq6U5jdXaVQKt3x57Xb0Ov1Vs2F7o19hizFPkN1wc8yZClb6jNSqbTGGLss9ORyOby9vYUiD7hzb12nTp1w/vx5DBs2DABMVs4E7ozMARCmasrlcpSUlJi1r1KpTKZzyuVys7aAO6N0lXGVX4uLi+Hr63vPc9aVRCJB+/bt76uN+qBWq5Gfn4927drB1dXV2umQHdBoNLh+qwRyuRxuHtb9TburTAwvL280afWQVfOg6rHPkKXYZ8gS/CxDlrK1PnP+/Plaxdllode+fXv88ccfVe4rLy9HmzZtIJFIoFAo0KdPH2Ff5X10lffbBQQE4M8//zQp2Crj7r4nLyAgwOzZesXFxbh586ZJW1Udq1AoIJFI4Ofndz9vGSKRCG5ubvfVRn1ydXW1qXzI1pVAIpFAZO0pVWJniMVi9l27wD5DlmKfIcvwswxZylb6TG2mbQJ2uhhL//79UVRUZDKV8fbt2/j111/RuXNnSKVShIeH45tvvjE5LjMzE4GBgWjdujUAICoqCk5OTti/f78Qo1QqcfjwYURHRwvboqOjcfToUWF0DgD27dsHJycnREZGAgD8/PzQrl07s+f0ZWZmIiIiolbDq0RERERERPXBLkf0Bg0ahJCQEMyYMQOJiYmQyWRIS0uDVCrFc889BwB4+eWXMWHCBCxatAhDhw7FsWPH8PXXX2PNmjVCOw8//DBGjRqFFStWwMnJCc2bN0dqaio8PT0xduxYIW7s2LH4+OOPMXXqVCQkJKCgoAArVqzA2LFjhWfoAcD06dMxZ84ctGnTBuHh4cjMzMTJkyexZcuWxrs4RERERET0wLPLQs/JyQlpaWlITk7GwoULodPp0KNHD2zdulW4P65Hjx5ISUnB2rVr8fnnn6Nly5ZYunSp2XPu5s+fD3d3d6xatQqlpaXo1q0b3n//fZPVOL28vPDhhx9iyZIlmDp1Ktzd3TFq1CgkJiaatDV8+HCo1Wqkp6cjLS0N/v7+WL9+PcLCwhr+ohAREREREf2XXRZ6AODj44N//vOf1cYMHDgQAwcOrDZGKpVi7ty5mDt3brVxgYGB+OCDD2rMa/To0Rg9enSNcURERERERA3FLu/RIyIiIiIiontjoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GBZ6REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERERERE5GIco9EpLSxEdHY2goCCcOnXKZN+OHTswZMgQhISE4KmnnsL3339vdnxxcTHmzZuHnj17IiwsDDNmzMCNGzfM4o4fP44xY8agS5cu6N+/P9LS0mA0Gk1ijEYj0tLS0K9fP3Tp0gVjxozBiRMn6vX9EhERERERVcchCr2NGzeioqLCbPuePXuwYMECDB06FOnp6QgNDcW0adPMCq+ZM2fiyJEjWLRoEVauXIm8vDzEx8dDr9cLMRcvXkRcXBx8fX2RmpqKiRMnYt26ddi8ebNJW+np6Vi3bh0mTZqE1NRU+Pr6IjY2FpcuXWqQ905ERERERPRXdl/oXbhwAZ988gmmT59utm/dunUYNmwYZs6ciV69euGtt95CSEgINmzYIMTk5OTg8OHD+Mc//oGYmBgMHDgQ77zzDn7//Xfs379fiMvIyECTJk2wevVqREREYNKkSYiNjcWmTZug1WoBAOXl5UhNTUVsbCwmTZqEiIgIrF69Gt7e3sjIyGj4i0FERERERAQHKPSWLl2KsWPHwt/f32T7pUuXkJ+fj6FDh5psj4mJQXZ2tlCcZWVlQS6XIzIyUogJCAhAcHAwsrKyhG1ZWVkYOHAgpFKpSVsqlQo5OTkA7kztLCkpMTmnVCrF4MGDTdoiIiIiIiJqSHZd6O3btw9nz57F1KlTzfYpFAoAMCsAAwMDodPphKmUCoUC/v7+EIlEJnEBAQFCG2VlZbh27RoCAgLMYkQikRBX+fWvcYGBgbh69So0Gk1d3yoREREREVGtia2dQF2p1WosX74ciYmJ8PDwMNuvVCoBAHK53GR75evK/SqVCp6enmbHe3l54fTp0wDuLNZSVVtSqRSurq4mbUmlUshkMrNzGo1GKJVKuLi4WPxegTuLvJSVldXp2PqkVqtNvhLVpPIXHDqdDnqDqIbohiV2MkKv19vEvyW6N/YZshT7DFmCn2XIUrbWZ4xGo9kgVVXsttB799130bRpUzzzzDPWTqVR6HQ65ObmWjsNQX5+vrVTIDshFoshcWsClUqFUrXWqrm4u0qhVLrjz2u3TRZbItvCPkOWYp+huuBnGbKULfWZu28nuxe7LPSuXLmCzZs3Y8OGDcJoW+VvzsrKylBaWgovLy8Ad0bjfH19hWNVKhUACPvlcjmuX79udg6lUinEVI74VZ6rklarhVqtNmlLq9WivLzcZFRPpVJBJBIJcXUhkUjQvn37Oh9fX9RqNfLz89GuXTu4urpaOx2yAxqNBtdvlUAul8PNw7q/aXeVieHl5Y0mrR6yah5UPfYZshT7DFlCrVbjjz/+QNu2bes804oeLLb2+ff8+fO1irPLQu/y5cvQ6XSYMmWK2b4JEyaga9euWLVqFYA7983dfc+cQqGARCKBn58fgDv302VnZ5sNgebl5aFDhw4AADc3N7Ro0UK4B+/uGKPRKLRf+TUvLw8dO3Y0OWfLli3v64eJSCSCm5tbnY+vb66urjaVD9m6EkgkEoisPaVK7AyxWMy+axfYZ8hS7DNUe57eD0GtF0NnI8snuLuI4eFW8wgNWZetfP6tzbRNwE4LveDgYHz00Ucm23Jzc5GcnIzFixcjJCQEfn5+aNeuHfbt24dBgwYJcZmZmYiIiBCGO6Ojo7Fx40ZkZ2ejd+/eAO4UamfOnMGLL74oHBcdHY2DBw/itddeg0QiEdqSy+UICwsDAHTr1g0eHh7Yu3evUOjpdDrs378f0dHRDXdBiIiIiKjWtHojzuQWQG+wdiaAi1SM7sHNWehRvbPLQk8ulyM8PLzKfZ07d0bnzp0BANOnT8ecOXPQpk0bhIeHIzMzEydPnsSWLVuE+LCwMERFRWHevHmYO3cuZDIZ1qxZg6CgIDz++ONCXFxcHHbv3o3Zs2dj3LhxOHv2LDIyMpCYmCgUjTKZDAkJCUhJSYGPjw86dOiATz/9FEVFRYiLi2vAK0JEREREltBo9VZfvIeoIdlloVdbw4cPh1qtRnp6OtLS0uDv74/169cLI3CV1q5di+TkZCxcuBB6vR5RUVGYP38+xOL/XZ62bdsiIyMDy5cvx5QpU+Dj44MZM2YgNjbWpK34+HgYjUZs3rwZhYWFCA4ORkZGhjBVlIiIiIiIqKE5TKEXHh6O33//3Wz76NGjMXr06GqP9fT0xLJly7Bs2bJq47p164bt27dXGyMSiZCQkICEhISakyYiIiIiImoAdv3AdCIiIiIiIjLHQo+IiIiIiMjBsNAjIiIiIiJyMCz0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNAjIiIiIiJyMCz0iIiIiIiIHAwLPSIisjkiiKydAhERkV0TWzsBIiKiv5JIXaDRqaDRGqyaR4XBCL2+wqo5EBER1QULPSIisjkVRiMKbpVCVaqzah7eHjLoKoxWzYGIiKguWOgREZFNqqgwQG/lIktvsO6IIhERUV3xHj0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIicjAs9IiIiIiIiBwMCz0iIiIiIiIHw0KPiIiIiIjIwbDQIyIiIiIiqoaTk/2VTXyOHhERERER2ZySMi1KNXprpwG9Xg9P74esnYbFWOgREREREZHNKdXo8XNuATRa6xZ7YiegfUuZVXOoCxZ6RERERERkkzRaPTTaCqvmIHYyArC/Qs/+JpsSkd0RQWTtFIiIiIgeKBzRI6IGJ5G6QKNTQaM1WDWPCoMRer11fytIRERE1BjsstDbu3cv/vWvf+HXX3+FSqVC27ZtMX78eDzzzDMQif43crBjxw689957uHr1Kvz9/ZGYmIj+/fubtFVcXIzk5GQcOHAAOp0Offr0wfz589GsWTOTuOPHj+Ptt99Gbm4umjZtinHjxiE+Pt7kfEajEenp6fjkk09QWFiI4OBgvP766wgNDW3Q60Fk6yqMRhTcKoWqVGfVPLw9ZNBVGK2aAxEREVFjsMupmx988AFcXV2RlJSEd999F9HR0ViwYAE2bNggxOzZswcLFizA0KFDkZ6ejtDQUEybNg0nTpwwaWvmzJk4cuQIFi1ahJUrVyIvLw/x8fHQ6/930+fFixcRFxcHX19fpKamYuLEiVi3bh02b95s0lZ6ejrWrVuHSZMmITU1Fb6+voiNjcWlS5ca9HoQ2YOKCgP0FUbr/jFYd0SRiIiIqLHY5Yjeu+++Cx8fH+F1REQEioqK8P777+OVV16Bk5MT1q1bh2HDhmHmzJkAgF69euHs2bPYsGED0tPTAQA5OTk4fPgwMjIyEBUVBQDw9/dHTEwM9u/fj5iYGABARkYGmjRpgtWrV0MqlSIiIgKFhYXYtGkTxo8fD6lUivLycqSmpiI2NhaTJk0CAHTv3h1PPPEEMjIysGjRoka7PkRERERE9GCzyxG9u4u8SsHBwSgpKUFZWRkuXbqE/Px8DB061CQmJiYG2dnZ0Gq1AICsrCzI5XJERkYKMQEBAQgODkZWVpawLSsrCwMHDoRUKjVpS6VSIScnB8CdqZ0lJSUm55RKpRg8eLBJW0RERERERA3NLgu9qvz8889o3rw5PDw8oFAoANwZnbtbYGAgdDqdMJVSoVDA39/f5D474E6xV9lGWVkZrl27hoCAALMYkUgkxFV+/WtcYGAgrl69Co1GU0/vlIiIiIiIqHp2OXXzr3766SdkZmZi7ty5AAClUgkAkMvlJnGVryv3q1QqeHp6mrXn5eWF06dPA7izWEtVbUmlUri6upq0JZVKIZOZPmNDLpfDaDRCqVTCxcWlzu/RaDSirKyszsfXF7VabfKVqCbl5eUA7qx4WWGw7oqXRoMBRqPBJv4t0b2xz5ClKn+ZqtPpoDdY93EuYicj9Ho9+4wNs6X+ArDPVEev10Ov10Ons+4D041OdxZys5WBG6PRaDZQVRW7L/SuX7+OxMREhIeHY8KECdZOp8HodDrk5uZaOw1Bfn6+tVMgO+Hi4gKxuy+02nKUlVr3FwSuEiO0Wi3y8q7YzA9rMsc+Q5YSi8WQuDWBSqVCqVpr1VzcXaVQKt3x57XbJgu7ke2wpf4CsM/cS+X36datW1b/Prm7SgF449KlSzbzPbr7lrJ7setCT6VSIT4+Ht7e3khJSYGT052ZqF5eXgDujMb5+vqaxN+9Xy6X4/r162btKpVKIaZyxK9yZK+SVquFWq02aUur1aK8vNxkVE+lUkEkEglxdSWRSNC+ffv7aqM+qNVq5Ofno127dnB1dbV2OmQHysvLcUOpg1Qqg5u7dX9z6uoig1QqhV8z/5qDyWrYZ8hSGo0G12+VQC6Xw83Dyn1GJoaXlzeatHrIqnnQvdlSfwHYZ6pzu0SPpk1L4VZu3eJK/N8RPT8/v/uaoVdfzp8/X6s4uy30NBoNEhISUFxcjG3btplMway8T06hUJjcM6dQKCCRSODn5yfEZWdnmw1/5uXloUOHDgAANzc3tGjRQrgH7+4Yo9EotF/5NS8vDx07djQ5Z8uWLe+7U4hEIri5ud1XG/XJ1dXVpvIhW6eDs5MIzk7OVs1C5OQEkciJfdcOiKBnnyGLiFAKiUQCkbWnboqdIRaL2WdsXolN9BeAfaY6xZqyOyN71v53/d9Cz8XFxSa+T7WZtgnYaaGn1+sxc+ZMKBQKbN26Fc2bNzfZ7+fnh3bt2mHfvn0YNGiQsD0zMxMRERHCUGd0dDQ2btyI7Oxs9O7dG8CdQu3MmTN48cUXheOio6Nx8OBBvPbaa5BIJEJbcrkcYWFhAIBu3brBw8MDe/fuFQo9nU6H/fv3Izo6uuEuBhGRA3JycobeAGj11r1HT6c3wGg0WjUHqh2J1AUanQoarXWfl1lhMEJv5X5LRATYaaG3ePFifP/990hKSkJJSYnJQ9A7deoEqVSK6dOnY86cOWjTpg3Cw8ORmZmJkydPYsuWLUJsWFgYoqKiMG/ePMydOxcymQxr1qxBUFAQHn/8cSEuLi4Ou3fvxuzZszFu3DicPXsWGRkZSExMFIpGmUyGhIQEpKSkwMfHBx06dMCnn36KoqIixMXFNdq1ISJyBAYYUarWQVlSbtU8ZBJnGAws9OxBhdGIglulUJXqrJqHt4cMugr2GSKyPrss9I4cOQIAWL58udm+gwcPonXr1hg+fDjUajXS09ORlpYGf39/rF+/XhiBq7R27VokJydj4cKF0Ov1iIqKwvz58yEW/+/StG3bFhkZGVi+fDmmTJkCHx8fzJgxA7GxsSZtxcfHw2g0YvPmzSgsLERwcDAyMjKEqaJERFR7RqMRBusOzsDA0Ty7UlFhgN7KRZbe2p2WiOi/7LLQ++6772oVN3r0aIwePbraGE9PTyxbtgzLli2rNq5bt27Yvn17tTEikQgJCQlISEioVX5EREREREQNwWEemE5ERERERER3sNAjIiIiIiJyMCz0iIiIiIiIHAwLPSJqcCJY/zlFRERERA8Su1yMhYjsC5+JRkRERNS4WOgRUYPjM9GIiIiIGhcLPSJqFHwmGhEREVHj4T16REREREREDoaFHhERERERkYNhoUdERERERORgWOgRERGR3eNjXIiITHExFiIiIrJ7fIwLEZEpFnpERERk9/gYFyIiUyz0iIiIyCHwMS5ERP/De/SIiIiI6IHCezrpQcARPSIiIiJ6oEikLtDoVNBorTwEDKDCYITeyveWkmNioUcWc3LiQDARERHZrwqjEQW3SqEq1Vk7FXh7yKCr4JRfqn8s9Mhint4P4XaJHsWaMmunAncXMTzcpNZOg4iIiOxMRYUBehsosPTWvrGUHBYLPbKYVm/EmdwC6K38c8lFKkb34OYs9IiIiIiI/oKFHtWJRquH3sAbmYmIiIjI8dnjAj4s9IiIiIjogWKPH9ofRHp9BUo1eqjL9VbNw0XqBInUxao51AULPSIiIiJ6oDg5OUNvALQ2sNqlTm+Akc9frJKuwogbt0pRVFJu1Tzk7hJU2OH3iIUeERERET1QDDCiVK2D0soFBADIJM4wGOyviGgseoP1F82pqLDPBXO4Tn4DuHDhAiZPnozQ0FBERkZixYoV0Gq11k6LiIiIiP7LaDTCYID1/9jhSNGDyB6n+3JEr54plUpMnDgR7dq1Q0pKCgoKCrB8+XJoNBosXLjQ2ukREREREdkFo9EInd5g9Sm2eoMYTk7OVs2hLljo1bPPPvsMpaWlWL9+Pby9vQEAFRUVWLx4MRISEtC8eXPrJkhEREREZAcMBiPKNHqrT7GVip1ggP2NvHLqZj3LyspCRESEUOQBwNChQ2EwGHDkyBHrJVaP7HHomoiIiIjsj8EGptja62I5HNGrZwqFAs8884zJNrlcDl9fXygUCitlVb8kMhdodCpotNa9MbXCYITeBlbLIiIiIiKyNSz06plKpYJcLjfb7uXlBaVSWac2dTodjEYjTp48eb/p3Tej0YgKAxDoo7f6zcNOIi1uXFXg1nWOMNo6fYURw7q7wWBwtWoezk4iqP68iJOFf1g1D6oZ+wxZin2GLGEr/QVgn6lOhcGI4d1cUWGw7jPsnJxEKLl1GeduAyKR9T936nS6WuXBQs8OVH4jbaFjiUQiODkBErHU2qmQHZGIRfD2kFk7DbIj7DNkKfYZsgT7i30QO4vgxe+TGZFIxELPGuRyOYqLi822K5VKeHl51anNsLCw+02LiIiIiIgeIFyMpZ4FBASY3YtXXFyMmzdvIiAgwEpZERERERHRg4SFXj2Ljo7G0aNHoVKphG379u2Dk5MTIiMjrZgZERERERE9KERGe10v1EYplUoMGzYM/v7+SEhIEB6Y/uSTT/KB6URERERE1ChY6DWACxcuYMmSJcjJyYG7uztGjBiBxMRESKVcwISIiIiIiBoeCz0iIiIiIiIHw3v0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNAjIiIiIiJyMCz0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNCjWrlw4QImT56M0NBQREZGYsWKFdBqtdZOi2zYxYsXsXDhQowYMQKdOnXC8OHDrZ0S2bC9e/fi5ZdfRnR0NEJDQzFixAh8/vnn4KNe6V4OHTqEF154Ab169cKjjz6KgQMHIjk5GcXFxdZOjexAaWkpoqOjERQUhFOnTlk7HbJRu3btQlBQkNmflStXWju1WhFbOwGyfUqlEhMnTkS7du2QkpKCgoICLF++HBqNBgsXLrR2emSjzp07h0OHDqFr164wGAz8wE7V+uCDD9CqVSskJSWhSZMmOHr0KBYsWIDr169j2rRp1k6PbFBRURG6dOmC8ePHw9vbG+fOnUNKSgrOnTuHzZs3Wzs9snEbN25ERUWFtdMgO/Hee+/B09NTeN28eXMrZlN7LPSoRp999hlKS0uxfv16eHt7AwAqKiqwePFiJCQk2E1np8Y1YMAADBo0CACQlJSE06dPWzkjsmXvvvsufHx8hNcREREoKirC+++/j1deeQVOTpyAQqZGjBhh8jo8PBxSqRQLFixAQUEB/2+ie7pw4QI++eQTzJ07F2+++aa10yE70LlzZ5P/o+wF/+ekGmVlZSEiIkIo8gBg6NChMBgMOHLkiPUSI5vGD+Zkiar+Aw0ODkZJSQnKysqskBHZo8r/p3Q6nXUTIZu2dOlSjB07Fv7+/tZOhahB8ZMY1UihUCAgIMBkm1wuh6+vLxQKhZWyIiJH9/PPP6N58+bw8PCwdipkwyoqKlBeXo5ff/0VGzZswIABA9C6dWtrp0U2at++fTh79iymTp1q7VTIjgwfPhzBwcEYOHAgUlNT7WbaL6duUo1UKhXkcrnZdi8vLyiVSitkRESO7qeffkJmZibmzp1r7VTIxvXv3x8FBQUAgD59+mDVqlVWzohslVqtxvLly5GYmMhfIFGt+Pr6Yvr06ejatStEIhG+++47rF27FgUFBXaxTgULPSIisinXr19HYmIiwsPDMWHCBGunQzYuLS0NarUa58+fx7vvvouXXnoJ77//Ppydna2dGtmYd999F02bNsUzzzxj7VTITvTp0wd9+vQRXkdFRUEmk+HDDz/ESy+9hGbNmlkxu5px6ibVSC6XV7lctVKphJeXlxUyIiJHpVKpEB8fD29vb6SkpPBeT6pRx44dERYWhtGjR2Pjxo04duwYvv32W2unRTbmypUr2Lx5M2bMmIHi4mKoVCrh/t+ysjKUlpZaOUOyF0OHDkVFRQVyc3OtnUqNOKJHNQoICDC7F6+4uBg3b940u3ePiKiuNBoNEhISUFxcjG3btpksZU1UG0FBQZBIJPjjjz+snQrZmMuXL0On02HKlClm+yZMmICuXbti+/btVsiMqOGw0KMaRUdHY9OmTSb36u3btw9OTk6IjIy0cnZE5Aj0ej1mzpwJhUKBrVu3cml8qpNffvkFOp2Oi7GQmeDgYHz00Ucm23Jzc5GcnIzFixcjJCTESpmRvcnMzISzszM6depk7VRqxEKPajR27Fh8/PHHmDp1KhISElBQUIAVK1Zg7Nix/DBG96RWq3Ho0CEAd6bMlJSUYN++fQCAnj172uXzaKjhLF68GN9//z2SkpJQUlKCEydOCPs6deoEqVRqveTIJk2bNg2PPvoogoKC4OLigt9++w0ZGRkICgoSnuFJVEkulyM8PLzKfZ07d0bnzp0bOSOyB3FxcQgPD0dQUBAA4ODBg9i+fTsmTJgAX19fK2dXM5HRaDRaOwmyfRcuXMCSJUuQk5MDd3d3jBgxAomJifzwRfd0+fJlDBw4sMp9H3300T3/w6UH04ABA3DlypUq9x08eJAjNGQmLS0NmZmZ+OOPP2A0GtGqVSsMHjwYcXFxXFGRauXYsWOYMGECPv/8c47oUZWWLl2Kf//737h+/ToMBgPatWuH0aNHY/z48RCJRNZOr0Ys9IiIiIiIiBwMlzMjIiIiIiJyMCz0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNAjIiIiIiJyMCz0iIiIiIiIHAwLPSIiIiIiIgfDQo+IiIiIiMjBsNAjIiJqJJcvX0ZQUJDZn9DQUDz55JNYv349SktLTY4ZP368EPf999/fs+3Ro0cLcceOHTPZFxQUhCeeeKJB3hMREdkmsbUTICIietC0adMGTz31FADAaDSisLAQWVlZSElJwb///W988skncHZ2NjlGLBZj586d6N+/v1l7586dw8mTJyEWi6HX6xvlPRARkW1joUdERNTI2rRpg+nTp5ts02q1GDNmDE6cOIEff/wRERERJvv79OmDH374AYWFhfDx8THZ9/nnn8PJyQlRUVH44YcfGjp9IiKyA5y6SUREZAOkUinCw8MBALdv3zbbP2rUKOh0Onz11Vcm23U6Hf71r38hMjISzZs3b5RciYjI9rHQIyIisgFarRY//vgjRCIRgoODzfZ37doV7du3x65du0y2f//99ygsLMQzzzzTWKkSEZEd4NRNIiKiRvbHH38gJSUFwJ179G7fvo3Dhw+joKAAr732Gvz9/as87plnnsHbb7+NkydPokuXLgDuTNv09vbGwIEDkZ2d3WjvgYiIbBsLPSIiokb2xx9/YP369Wbb+/fvj969e9/zuBEjRmD16tXYuXMnunTpgoKCAhw+fBjPP/88pFJpQ6ZMRER2hlM3iYiIGllUVBR+//134c9//vMfbNy4EWfPnsW4cePwyy+/VHlc06ZN0bdvX2RmZqK8vBxffvklKioqOG2TiIjMsNAjIiKysiZNmmDgwIFYunQp1Go11q5de8/YZ555BiqVCt988w127dqFzp07o2PHjo2XLBER2QUWekRERDai8r67U6dO3TOmb9++8PX1xcqVK5Gfn49Ro0Y1VnpERGRHWOgRERHZCJVKBQAwGAz3jHF2dsbTTz+NgoICyGQyDB8+vLHSIyIiO8LFWIiIiGzE+++/DwB47LHHqo2bPHkyQkND4eXlBblc3hipERGRnWGhR0RE1MjufrwCACiVShw/fhy//vorvLy8MGfOnGqPb9q0KQYNGtTQaRIRkR1joUdERNTI/vp4BalUiocffhjjxo3DlClT0LJlSytmR0REjkBkNBqN1k6CiIiIiIiI6g8XYyEiIiIiInIwLPSIiIiIiIgcDAs9IiIiIiIiB8NCj4iIiIiIyMGw0CMiIiIiInIwLPSIiIiIiIgcDAs9IiIiIiIiB8NCj4iIiIiIyMGw0CMiIiIiInIwLPSIiIiIiIgcDAs9IiIiIiIiB8NCj4iIiIiIyMGw0CMiIiIiInIw/w+Ur7BTF6vruwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# check the feature wise distribution by Test, Train, Validation Sets\n",
        "for col in X_test.columns[:]:\n",
        "    plt.figure(figsize=(10,4))\n",
        "    plt.hist(X_val[col], bins=20, alpha=0.5, label=\"validation\", color='b')\n",
        "    plt.hist(X_test[col], bins=20, alpha=0.5, label=\"test\", color='b')\n",
        "    plt.hist(X_train[col], bins=20, alpha=0.5, label=\"train\", color='b')\n",
        "    plt.xlabel(col, size=14)\n",
        "    plt.ylabel(\"Count\", size=14)\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.title(\"{} distribution\".format(col))\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b25570d",
      "metadata": {
        "id": "0b25570d"
      },
      "source": [
        "#### Do the training and test sets have the same data?\n",
        "\n",
        "\n",
        "##### Above graphs shows the distribution by sets [Test, Train, Validation]\n",
        "* Yes!, all the important features in Test and Validation sets are following same distribution as Train set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "f8210208",
      "metadata": {
        "scrolled": false,
        "id": "f8210208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "ebd477a9-ae0f-4870-9e83-f45cc4434775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<----------- Model Performance with Test set ----------->\n",
            "model: LogisticRegression(random_state=0)\n",
            "Accuracy_score: 0.7619547003773068\n",
            "Precission_score: 0.75\n",
            "Recall_score: 0.779747533972546\n",
            "F1-score: 0.7645845310967566\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# lets build a Logistic Model and check the model performance with Test and Validation Sets.\n",
        "lr = LogisticRegression(random_state=0)\n",
        "lr.fit(X_train, y_train)\n",
        "lr_y_predict = lr.predict(X_test)\n",
        "\n",
        "print(\"<----------- Model Performance with Test set ----------->\")\n",
        "print(f'model: {str(lr)}')\n",
        "print(f'Accuracy_score: {accuracy_score(y_test,lr_y_predict)}')\n",
        "print(f'Precission_score: {precision_score(y_test,lr_y_predict)}')\n",
        "print(f'Recall_score: {recall_score(y_test,lr_y_predict)}')\n",
        "print(f'F1-score: {f1_score(y_test,lr_y_predict)}')\n",
        "\n",
        "\n",
        "\n",
        "cm = confusion_matrix(y_test,lr_y_predict)\n",
        "plt.figure(figsize=(10,5))\n",
        "ax = sns.heatmap(cm/np.sum(cm),fmt='.2%', annot=True, cmap='Blues')\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "ax.xaxis.set_ticklabels(['No HeartDisease','HeartDisease'])\n",
        "ax.yaxis.set_ticklabels(['No HeartDisease','HeartDisease'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "7e07f0a2",
      "metadata": {
        "scrolled": false,
        "id": "7e07f0a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "outputId": "8900a3ec-2977-4ca6-ee2f-c6fca8134725"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<----------- Model Performance with Validation set ----------->\n",
            "model: LogisticRegression(random_state=0)\n",
            "Accuracy_score: 0.761043611200515\n",
            "Precission_score: 0.7492351187018318\n",
            "Recall_score: 0.7816565656565656\n",
            "F1-score: 0.7651025291174783\n",
            "--------------------------------------------------------------\n",
            "Model AUC Score on Training Data: 0.840198414422887\n",
            "Model AUC Score on Test Data: 0.8387856212117553\n",
            "Model AUC Score on Validation Data: 0.8384597153329627\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Make predictions using the validation set\n",
        "y_val_pred = lr.predict(X_val)\n",
        "print(\"\\n<----------- Model Performance with Validation set ----------->\")\n",
        "print(f'model: {str(lr)}')\n",
        "print(f'Accuracy_score: {accuracy_score(y_val,y_val_pred)}')\n",
        "print(f'Precission_score: {precision_score(y_val,y_val_pred)}')\n",
        "print(f'Recall_score: {recall_score(y_val,y_val_pred)}')\n",
        "print(f'F1-score: {f1_score(y_val,y_val_pred)}')\n",
        "print(\"--------------------------------------------------------------\")\n",
        "print(f\"Model AUC Score on Training Data: {roc_auc_score(y_train, lr.predict_proba(X_train)[:,1])}\")\n",
        "print(f\"Model AUC Score on Test Data: {roc_auc_score(y_test, lr.predict_proba(X_test)[:,1])}\")\n",
        "print(f\"Model AUC Score on Validation Data: {roc_auc_score(y_val, lr.predict_proba(X_val)[:,1])}\")\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "cm = confusion_matrix(y_val,y_val_pred)\n",
        "plt.figure(figsize=(10,5))\n",
        "ax = sns.heatmap(cm/np.sum(cm),fmt='.2%', annot=True, cmap='Blues')\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "ax.xaxis.set_ticklabels(['No HeartDisease','HeartDisease'])\n",
        "ax.yaxis.set_ticklabels(['No HeartDisease','HeartDisease'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07088780",
      "metadata": {
        "id": "07088780"
      },
      "source": [
        "### Logistic Model Performance is very stable across all the sets (`Test and Valiadtion` sets) and the scores are very close among all the metrics like `Accuracy, Precision, Recall`.\n",
        "* After `OverSampling, the model performance boosted` and the `scores are very stable` when we checked with Validation set as well, This seems to be a `very good and stable model to predict HeartDisease` as the model performed very well on validation set aswell."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lets try Tree Based Model to find a better model - Random Forest "
      ],
      "metadata": {
        "id": "qtDpTu18QXvV"
      },
      "id": "qtDpTu18QXvV"
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "id": "AijnBl0E6DKZ",
        "outputId": "966580b0-e30e-4d82-dc68-703b8eda8a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "id": "AijnBl0E6DKZ",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        AgeCategory  Stroke  Diabetic_Yes  KidneyDisease  Smoking  SkinCancer  \\\n",
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              "\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "id": "TGoA4Deg6Emo",
        "outputId": "852c9008-f175-4e3b-d3ac-8b8f33f8885e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "TGoA4Deg6Emo",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(447405, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using some hyperparameters to find the best model in RandomForestClassifier\n",
        "forest = RandomForestClassifier(n_estimators=28, max_depth=10, max_features='sqrt',criterion='entropy',random_state=0)\n",
        "forest.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "fb3msvAoQS4h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "a06fa718-5a46-41c6-cbaf-0d9b29167947"
      },
      "id": "fb3msvAoQS4h",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=10, n_estimators=28,\n",
              "                       random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, n_estimators=28,\n",
              "                       random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, n_estimators=28,\n",
              "                       random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Model Score on Training Data: {forest.score(X_train,y_train)}\")\n",
        "print(f\"Model Score on Test Data: {forest.score(X_test,y_test)}\")\n",
        "print(f\"Model Score on Validation Data: {forest.score(X_val, y_val)}\")\n",
        "print(\"--------------------------------------------------------------\")\n",
        "print(f\"Model AUC Score on Training Data: {roc_auc_score(y_train, forest.predict_proba(X_train)[:,1])}\")\n",
        "print(f\"Model AUC Score on Test Data: {roc_auc_score(y_test, forest.predict_proba(X_test)[:,1])}\")\n",
        "print(f\"Model AUC Score on Validation Data: {roc_auc_score(y_val, forest.predict_proba(X_val)[:,1])}\")"
      ],
      "metadata": {
        "id": "jqEJjQZlQS13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd8f5de1-d104-4acd-86fe-51f1e23a8a36"
      },
      "id": "jqEJjQZlQS13",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Score on Training Data: 0.7706798091214895\n",
            "Model Score on Test Data: 0.7682013519213013\n",
            "Model Score on Validation Data: 0.7661731573865465\n",
            "--------------------------------------------------------------\n",
            "Model AUC Score on Training Data: 0.8495585317123676\n",
            "Model AUC Score on Test Data: 0.8466466529846044\n",
            "Model AUC Score on Validation Data: 0.8449129812668792\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model Performance is very stable across all the sets (`Train, Test and Valiadtion` sets)\n",
        "* This is the best model so far compare to Logistic as we can see the RandomForest model AUC score is ~85% where as Logistic model AUC score is just 83%.\n",
        "* Morever RandomForest model performance is pretty stable compare to Logistic model on every dataset like (Train, Test and Validation).\n",
        "* Based on the above performance, selecting the RandomForestClassifier Model."
      ],
      "metadata": {
        "id": "OfpnRrLCSsfd"
      },
      "id": "OfpnRrLCSsfd"
    },
    {
      "cell_type": "code",
      "source": [
        "######Tuning Random forest with different parameters\n",
        "forest2 = RandomForestClassifier(n_estimators=50, max_depth=20, max_features='sqrt',criterion='entropy',random_state=0)\n",
        "forest2.fit(X_train,y_train)\n",
        "print(f\"Model Score on Training Data: {forest2.score(X_train,y_train)}\")\n",
        "print(f\"Model Score on Test Data: {forest2.score(X_test,y_test)}\")\n",
        "print(f\"Model Score on Validation Data: {forest2.score(X_val, y_val)}\")\n",
        "print(\"--------------------------------------------------------------\")\n",
        "print(f\"Model AUC Score on Training Data: {roc_auc_score(y_train, forest2.predict_proba(X_train)[:,1])}\")\n",
        "print(f\"Model AUC Score on Test Data: {roc_auc_score(y_test, forest2.predict_proba(X_test)[:,1])}\")\n",
        "print(f\"Model AUC Score on Validation Data: {roc_auc_score(y_val, forest2.predict_proba(X_val)[:,1])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZeosVU6d3IJ7",
        "outputId": "52948025-df6b-4905-fe70-6e2bc25c172c"
      },
      "id": "ZeosVU6d3IJ7",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Score on Training Data: 0.8140543802594964\n",
            "Model Score on Test Data: 0.8018056014681911\n",
            "Model Score on Validation Data: 0.8004103636948825\n",
            "--------------------------------------------------------------\n",
            "Model AUC Score on Training Data: 0.8995244493825688\n",
            "Model AUC Score on Test Data: 0.8836961527913156\n",
            "Model AUC Score on Validation Data: 0.8833437815378364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_idx = forest.feature_importances_.argsort()\n",
        "features = X_train.columns.tolist()\n",
        "result = sorted(zip(features, forest.feature_importances_), key = lambda x: x[1], reverse=False)\n",
        "plt.barh([x[0] for x in result], [x[1] for x in result])"
      ],
      "metadata": {
        "id": "64zvic4EQSzB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "ae3062d9-704d-4535-c22e-f350372fc9c3"
      },
      "id": "64zvic4EQSzB",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 12 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Observations:\n",
        "\n",
        "* Above charts shows the variable important in descending order.\n",
        "* Age category, General health, Diabetic_YES, Stroke and KidneyDisease are the significant important features to predict the Heart Disease.\n",
        "* It is strange that `BMI` is `not` among the `TOP important features`.\n",
        "* If closely observed, `Health related issues like Diabetic, Stroke, Kidney Disease` are the very important features and people suffered with such issues have high chances of getting heart Disease followed by `AGE, Gender and Smoking`.\n",
        "* Let's try to understand the behaviour using the Shap Values.\n",
        "\n",
        "#### Above Feature Importance are almost as same the feature importance obtained from the Permutatution Importance order."
      ],
      "metadata": {
        "id": "-60DyZVGUCO2"
      },
      "id": "-60DyZVGUCO2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pickling the above developed RandomForest Model to predict Heart Disease and use this Pickle file in Streamlit to run the App."
      ],
      "metadata": {
        "id": "GtM4oMxV9NdQ"
      },
      "id": "GtM4oMxV9NdQ"
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets pickle the model to make use in UI\n",
        "import pickle\n",
        "filename = \"rf_model_to_predict_heartDisease\"\n",
        "pickle.dump(forest, open(filename, \"wb\"))"
      ],
      "metadata": {
        "id": "xi-OAfAvVAdA"
      },
      "id": "xi-OAfAvVAdA",
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets pickle the tuned model to make use of it in the UI\n",
        "filename_tuned = \"rf_model_to_predict_heartDisease_tuned\"\n",
        "pickle.dump(forest2, open(filename_tuned, \"wb\"))"
      ],
      "metadata": {
        "id": "5s9-rHXGSakE"
      },
      "execution_count": 69,
      "outputs": [],
      "id": "5s9-rHXGSakE"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "id": "uJ4KWbKZQStn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb4b59b8-d2ad-4e07-93b9-77daf84742e7"
      },
      "id": "uJ4KWbKZQStn",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (572 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.4/572.4 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from shap) (1.5.3)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.9/dist-packages (from shap) (0.56.4)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.9/dist-packages (from shap) (23.1)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.9/dist-packages (from shap) (4.65.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from shap) (1.10.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.9/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from shap) (1.22.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from numba->shap) (67.7.2)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.9/dist-packages (from numba->shap) (0.39.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.41.0 slicer-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Interpretability using SHAP:"
      ],
      "metadata": {
        "id": "yzAeoh8zewhw"
      },
      "id": "yzAeoh8zewhw"
    },
    {
      "cell_type": "code",
      "source": [
        "import shap  # package used to calculate Shap values\n"
      ],
      "metadata": {
        "id": "bVtj8Y-oTNDi"
      },
      "id": "bVtj8Y-oTNDi",
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create object that can calculate shap values\n",
        "explainer = shap.TreeExplainer(forest)\n",
        "\n",
        "# calculate shap values. This is what we will plot.\n",
        "# Calculate shap_values for all of val_X rather than a single row, to have more data for plot.\n",
        "shap_values = explainer.shap_values(X_test)\n",
        "\n",
        "# Make plot. Index of [1] is explained in text below.\n",
        "shap.summary_plot(shap_values[1], X_test)"
      ],
      "metadata": {
        "id": "VVi7_pQhQSm4"
      },
      "id": "VVi7_pQhQSm4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values[0], X_test)"
      ],
      "metadata": {
        "id": "CrHEWvig0V3T"
      },
      "id": "CrHEWvig0V3T",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n"
      ],
      "metadata": {
        "id": "nX6r9yaiFokl"
      },
      "id": "nX6r9yaiFokl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# instance_to_explain = X_test.iloc[0]\n",
        "shap.plots.force(explainer.expected_value[0], shap_values[0])"
      ],
      "metadata": {
        "id": "-x_15IfMFSt3"
      },
      "id": "-x_15IfMFSt3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Observations on SHAP values:\n",
        "\n",
        "* As expected, with `increase in Age`, the `chances of getting HeartDisease are also High` compare to younger people\n",
        "* GenHealth `(General Health) shows very realastic trend` that if `someones general health is poor/bad`, they are `high prone to Heart Disease`\n",
        "* `Male_Gender, Smoking, BMI, Asthma` follows the same trend, if they are `Male / have smoking habit / has high BMI value / has Asthma problem`, they are `highly prone` to diagonsed with Heart Disease.\n",
        "* `Stroke` & `KidneyDisease` shows realastic trend that If someone suffered with Stroke / KideneyDisease in the past has high chances of getting Heart Disease.\n",
        "* It is `interesting behaviour about AlcholDrinking habit`, irrespective of `whether one drink or not`, `few cannot escape from diagnosed` with Heart Disease.\n",
        "* As we infered from model feature importance, if `someone is facing critical Health issues` then they are `highly prone to HeartDisease`.\n",
        "#### All these observations are algined with the above RandomForest model variable importance."
      ],
      "metadata": {
        "id": "u0sK1gCNUQ8a"
      },
      "id": "u0sK1gCNUQ8a"
    },
    {
      "cell_type": "markdown",
      "id": "aec0adf0",
      "metadata": {
        "id": "aec0adf0"
      },
      "source": [
        "### Overall Conclusion:\n",
        "* Above Dataset We have both numerical and categorical features and the numerical features have very extreme outliers.\n",
        "* There are no missing data in the dataset, so no missing imputation required.\n",
        "* I have used IQR method to impute outliers of numerical features and imputed with mean as most of the features median is 0 but positively skewed, so I assumed imputing outliers with Mean make sense than imputing with ZERO.\n",
        "* As the scales are different for each feature, I Normalized the data to bring down all the features to same scale.\n",
        "* As BMI_values are not seems to be making as their is no significant difference between adults with or without Heart Disease eventhough the BMI is very High, so I have created a BMI class which showed more realistic distribution.\n",
        "* I have used Encoders to change Categorical features to Numeric values.\n",
        "* I have used OLS, Pearson correlation, PermutationImportance to find important features to identify Heart Disease.\n",
        "* Data has multicollinear features, I have used Pearson correlation and Variance Inflation Factor (VIF) to remove multicolinear features from the predictors.\n",
        "* As the data is unbalanced, I used Oversampling technique to imporve my Target distribution. \n",
        "* OverSampling improved my performance of the model and I have used Logistic Regression model to build a stable model to predict Heart Disease.\n",
        "* Tried Random Forest model and the AUC scores, and Model Performance Metrics are very good and very stable across test and validation sets, this shows the stability of the model to identify Heart Disease.\n",
        "\n",
        "* As expected, with `increase in Age`, the `chances of getting HeartDisease are also High` compare to younger people\n",
        "* GenHealth `(General Health) shows very realastic trend` that if `someones general health is poor/bad`, they are `high prone to Heart Disease`\n",
        "* `Male_Gender, Smoking, BMI, Asthma` follows the same trend, if they are `Male / have smoking habit / has high BMI value / has Asthma problem`, they are `highly prone` to diagonsed with Heart Disease.\n",
        "* `Stroke` & `KidneyDisease` shows realastic trend that If someone suffered with Stroke / KideneyDisease in the past has high chances of getting Heart Disease.\n",
        "* It is `interesting behaviour about AlcholDrinking habit`, irrespective of `whether one drink or not`, `few cannot escape from diagnosed` with Heart Disease.\n",
        "* As we infered from model feature importance, if `someone is facing critical Health issues` then they are `highly prone to HeartDisease`.\n",
        "#### All these observations are algined with the above RandomForest model variable importance."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "632b2960",
      "metadata": {
        "id": "632b2960"
      },
      "source": [
        "### Refernces:\n",
        "1. Scikit learn Documentation\n",
        "2. Referred Medium Articles\n",
        "3. Referred Analytics Vidhya Articles\n",
        "4. Referred Towards Data Science Articles\n",
        "5. Referred Kaggle Notebooks.\n",
        "6. https://github.com/aiskunks/Skunks_Skool/blob/main/H2O_AutoML_IPYNB/glm_h2oworld_demo.ipynb\n",
        "7.https://github.com/aiskunks/Skunks_Skool/blob/main/H2O_AutoML_IPYNB/glrm.census.labor.violations.ipynb\n",
        "8.https://github.com/aiskunks/Skunks_Skool/blob/main/H2O_AutoML_IPYNB/glrm.census.labor.violations.ipynb\n",
        "9.https://github.com/aiskunks/Skunks_Skool/blob/main/H2O_AutoML_IPYNB/glrm.walking.gait.ipynb\n",
        "10. https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/\n",
        "11.https://github.com/aiskunks/Skunks_Skool/tree/main/I2SL\n",
        "12. permutation importance is refered from scikit-learn permutation handling concept document https://scikit-learn.org/stable/modules/permutation_importance.html\n",
        "13. modeling is refered from scikit-learn logistic regression officiakl documentatio\n",
        "14. Outlier Imputation is refered from sklearn \"logistic-regression-using-python-and-excel\" explanation\n",
        "15. Refered Kaggle contributions for oversampling\n",
        "16. Outlier handling is referec from analytics vidhya outlier handling theoritical explanation https://www.analyticsvidhya.com/blog/2021/05/detecting-and-treating-outliers-treating-the-odd-one-out/\n",
        "17. Shap Analysis is refered from https://www.analyticsvidhya.com/blog/2021/11/model-explainability/\n",
        "\n",
        "**I have used python libraries to do I have developed my own functions to plot the charts, to compute required metrics, to calculate required information and Every single line of code was written by myself and not copied from anywhere. for Data Exploration, I have referred to few Towards Data Science, Kaggle, Analytics vidya articles and have developed my knowledge from it. For Feature Scaling, I rerferred to Analytics Vidya article to understand more about encoders.**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "047fe8e6",
      "metadata": {
        "id": "047fe8e6"
      },
      "source": [
        "### Copyright\n",
        "\n",
        "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:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
        "\n",
        "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."
      ]
    }
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