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readme.md

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+# Streamlit Web App For Sales Prediction
+# LP4 Embedding a machine learning model in a GUI
+*short project description*
+
+## Summary
+| Code      | Name        | Published Article |  Deployed App |
+|-----------|-------------|:-------------:|------:|
+| LP4 Embedding a machine learning model in a GUI|streamlit-web-app-for-sales-prediction| [Article]https://medium.com/@otchie.sonny/building-streamlit-web-app-for-sales-prediction-with-facebook-prophet-26c84ed8f625 | [Deployed App](http://localhost:8503) |
+|  
+##  Description
+
+Favorita Stores Sales Prediction App
+This web application allows users to predict sales for Favorita stores based on historical data using the Facebook Prophet machine learning model. Users can input a specific date, and the app will provide a sales prediction for that date.
+
+Setup
+Make sure you have Python installed on your system.
+
+Clone this repository to your local machine.
+
+Copy code
+
+git clone <repository-url>
+Navigate to the project directory.
+
+Copy code
+
+cd <project-directory>
+Set up a virtual environment (optional but recommended).
+
+Copy code
+
+python -m venv myenv
+Activate the virtual environment.
+
+On Windows:
+
+Copy code
+
+myenv\Scripts\activate
+On macOS and Linux:
+
+Copy code
+
+source myenv/bin/activate
+Install the required packages.
+
+Copy code
+
+pip install -r requirements.txt
+Usage
+Run the Streamlit app.
+
+Copy code
+
+streamlit run sales_prediction_app.py
+
+The app will start a local web server, and you can access it by opening the provided URL in your browser.
+
+Once the app is loaded, you can select a specific date from the sidebar.
+
+The app will then display the predicted sales for that date based on the historical data and the trained Prophet model.
+
+Contributing
+Contributions to this project are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
+
+Favorita Stores Sales Prediction App
+This web application allows users to predict sales for Favorita stores based on historical data using the Facebook Prophet machine learning model. Users can input a specific date, and the app will provide a sales prediction for that date.
+
+Setup
+Make sure you have Python installed on your system.
+
+Clone this repository to your local machine.
+
+
+Copy code
+
+git clone <repository-url>
+Navigate to the project directory.
+
+
+Copy code
+
+cd <project-directory>
+Set up a virtual environment (optional but recommended).
+
+
+Copy code
+
+python -m venv myenv
+Activate the virtual environment.
+
+On Windows:
+
+
+Copy code
+
+myenv\Scripts\activate
+On macOS and Linux:
+
+
+Copy code
+source myenv/bin/activate
+Install the required packages.
+
+
+Copy code
+
+pip install -r requirements.txt
+Usage
+Run the Streamlit app.
+
+
+Copy code
+
+streamlit run sales_prediction_app.py
+The app will start a local web server, and you can access it by opening the provided URL in your browser.
+
+Once the app is loaded, you can select a specific date from the sidebar.
+
+The app will then display the predicted sales for that date based on the historical data and the trained Prophet model.
+
+Contributing
+Contributions to this project are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
+
+
+Feel free to customize the read.md file based on your specific project details and requirements.
+Please make sure to update tests as appropriate. Contact me via my email: [email protected]
+
+## Author
+Sonny Agorvor-Otchie.
+
+

requirements.txt

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+altair==5.0.1
+attrs==23.1.0
+blinker==1.6.2
+cachetools==5.3.1
+category==0.1.0
+category-encoders==2.6.1
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+cmdstanpy==1.1.0
+colorama==0.4.6
+contourpy==1.1.0
+convertdate==2.4.0
+cycler==0.11.0
+decorator==5.1.1
+encoders==0.0.3
+ephem==4.1.4
+fonttools==4.40.0
+gitdb==4.0.10
+GitPython==3.1.31
+holidays==0.27.1
+idna==3.4
+importlib-metadata==6.7.0
+importlib-resources==5.12.0
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.17.3
+kiwisolver==1.4.4
+LunarCalendar==0.0.9
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib==3.7.1
+mdurl==0.1.2
+numpy==1.25.0
+packaging==23.1
+pandas==2.0.2
+patsy==0.5.3
+Pillow==9.5.0
+plotly==5.15.0
+prophet==1.1.4
+protobuf==4.23.3
+pyarrow==12.0.1
+pydeck==0.8.1b0
+Pygments==2.15.1
+PyMeeus==0.5.12
+Pympler==1.0.1
+pyparsing==3.1.0
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+requests==2.31.0
+rich==13.4.2
+rust_category==0.2.0
+scikit-learn==1.2.2
+scipy==1.10.1
+seaborn==0.12.2
+six==1.16.0
+smmap==5.0.0
+statsmodels==0.14.0
+streamlit==1.23.1
+tenacity==8.2.2
+threadpoolctl==3.1.0
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.2
+tqdm==4.65.0
+typing_extensions==4.6.3
+tzdata==2023.3
+tzlocal==4.3.1
+urllib3==2.0.3
+validators==0.20.0
+watchdog==3.0.0
+zipp==3.15.0