app.py

.gitignore

@@ -0,0 +1,164 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+/.env
+/train.csv
+/data.csv
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Michael Dzifa Kumassah
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

@@ -0,0 +1,131 @@
+import pandas as pd
+import streamlit as st
+import numpy as np
+import pickle
+# streamlit import sklearn
+from PIL import Image
+import os
+import xgboost as xgb
+
+# model = xgb.XGBRegressor()
+# model = model.load_model('export/xg_model.json')
+
+# Load the saved components:
+with open(".\export\dt_model.pkl", "rb") as f:
+    components = pickle.load(f)
+
+# Extract the individual components
+num_imputer = components["num_imputer"]
+cat_imputer = components["cat_imputer"]
+encoder = components["encoder"]
+scaler = components["scaler"]
+dt_model = components["models"]
+
+# Create the app
+
+st.set_page_config(
+    layout="wide"
+)
+
+
+# # Add an image or logo to the app
+# image = Image.open('copofav.jpg')
+
+# # Open the image file
+# st.image(image)
+
+
+# add app title
+st.title("SALES PREDICTION APP")
+
+
+# Add some text
+st.write("Please ENTER the relevant data and CLICK Predict.")
+
+# Create the input fields
+input_data = {}
+col1, col2, col3 = st.columns(3)
+with col1:
+    input_data['store_nbr'] = st.slider(
+        "Store Number", min_value=0, step=1, max_value=54)
+    input_data['family'] = st.selectbox("Products Family", ['AUTOMOTIVE', 'BABY CARE', 'BEAUTY', 'BEVERAGES', 'BOOKS',
+                                                            'BREAD/BAKERY', 'CELEBRATION', 'CLEANING', 'DAIRY', 'DELI', 'EGGS',
+                                                            'FROZEN FOODS', 'GROCERY I', 'GROCERY II', 'HARDWARE',
+                                                            'HOME AND KITCHEN I', 'HOME AND KITCHEN II', 'HOME APPLIANCES',
+                                                            'HOME CARE', 'LADIESWEAR', 'LAWN AND GARDEN', 'LINGERIE',
+                                                            'LIQUOR,WINE,BEER', 'MAGAZINES', 'MEATS', 'PERSONAL CARE',
+                                                            'PET SUPPLIES', 'PLAYERS AND ELECTRONICS', 'POULTRY',
+                                                            'PREPARED FOODS', 'PRODUCE', 'SCHOOL AND OFFICE SUPPLIES',
+                                                            'SEAFOOD'])
+    input_data['onpromotion'] = st.number_input(
+        "Discount Amt On Promotion", step=1)
+
+with col2:
+    input_data['state'] = st.selectbox("State", ['Santa Elena', 'Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura',
+                                                 'Santo Domingo de los Tsachilas', 'Bolivar', 'Tungurahua',
+                                                 'Guayas', 'Los Rios', 'Azuay', 'Loja', 'El Oro', 'Esmeraldas',
+                                                 'Manabi', 'Pastaza'])
+    input_data['store_type'] = st.radio(
+        "Store Type", options=['A', 'B', 'C', 'D', 'E'], horizontal=True)
+    input_data['cluster'] = st.number_input("Cluster", step=1)
+
+with col3:
+    input_data['month'] = st.slider("Month", 1, 12)
+    input_data['day'] = st.slider("Day", 1, 31)
+    input_data['dcoilwtico'] = st.slider(
+        "DCOILWTICO", min_value=29, step=1, max_value=108)
+
+  # Create a button to make a prediction
+if st.button("Predict"):
+    # Convert the input data to a pandas DataFrame
+    input_df = pd.DataFrame([input_data])
+
+    # # categorizing the products
+    # food_families = ['BEVERAGES', 'BREAD/BAKERY', 'FROZEN FOODS', 'MEATS', 'PREPARED FOODS', 'DELI','PRODUCE', 'DAIRY','POULTRY','EGGS','SEAFOOD']
+    # home_families = ['HOME AND KITCHEN I', 'HOME AND KITCHEN II', 'HOME APPLIANCES']
+    # clothing_families = ['LINGERIE', 'LADYSWARE']
+    # grocery_families = ['GROCERY I', 'GROCERY II']
+    # stationery_families = ['BOOKS', 'MAGAZINES','SCHOOL AND OFFICE SUPPLIES']
+    # cleaning_families = ['HOME CARE', 'BABY CARE','PERSONAL CARE']
+    # hardware_families = ['PLAYERS AND ELECTRONICS','HARDWARE']
+    # others_families = ['AUTOMOTIVE', 'BEAUTY','CELEBRATION', 'LADIESWEAR', 'LAWN AND GARDEN', 'LIQUOR,WINE,BEER',  'PET SUPPLIES']
+
+    # # Apply the same preprocessing steps as done during training
+    # input_df['products'] = np.where(input_df['products'].isin(food_families), 'FOODS', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(home_families), 'HOME', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(clothing_families), 'CLOTHING', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(grocery_families), 'GROCERY', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(stationery_families), 'STATIONERY', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(cleaning_families), 'CLEANING', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(hardware_families), 'HARDWARE', input_df['products'])
+    # input_df['products'] = np.where(input_df['products'].isin(others_families), 'OTHERS', input_df['products'])
+
+    categorical_columns = ['family', 'state', 'store_type']
+    numerical_columns = ['store_nbr', 'onpromotion',
+                         'cluster', 'dcoilwtico', 'month', 'day']
+    # Impute missing values
+    input_df_cat = input_df[categorical_columns].copy()
+    input_df_num = input_df[numerical_columns].copy()
+    input_df_cat_imputed = cat_imputer.fit_transform(input_df_cat)
+    input_df_num_imputed = num_imputer.fit_transform(input_df_num)
+
+    # Encode categorical features
+    input_df_cat_encoded = encoder.fit(input_df_cat_imputed)
+    input_df_cat_encoded = pd.DataFrame(encoder.transform(input_df_cat_imputed).toarray(),
+                                        columns=encoder.get_feature_names_out(categorical_columns))
+
+    # Scale numerical features
+    input_df_num_scaled = scaler.fit_transform(input_df_num_imputed)
+    input_df_num_sc = pd.DataFrame(
+        input_df_num_scaled, columns=numerical_columns)
+
+    # Combine encoded categorical features and scaled numerical features
+    input_df_processed = pd.concat(
+        [input_df_num_sc, input_df_cat_encoded], axis=1)
+
+    # Make predictions using the trained model
+    predictions = dt_model.predict(input_df_processed)
+    # predictions = model.predict(input_df_processed)
+
+    # Display the predicted sales value to the user:
+    st.write("Predicted Sales:", predictions[0])

export/dt_model.pkl

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+oid sha256:0448a70603e09a5cec325e55ffca7f0b0da4d67696e8fde210c89647adeef74b
+size 290207

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