predictive_analytics_forecasting

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Predictive Analytics and Forecasting

Overview

This project focuses on developing and validating sales forecasting models for a Swiss retail chain model data Example. It includes a comprehensive dataset and a variety of tools for time series analysis and model training. The project features:

• Data Generation: A script to create realistic sales data.
• Forecast Model: A Python script using Scikit-learn to build and validate forecasting models.
• Jupyter Notebook: An interactive notebook for exploratory data analysis (EDA) and model training.

Project Structure

The directory structure of this repository is as follows:

predictive_analytics_forecasting/ │ ├── README.md # This file ├── data/ # Directory containing data files │ └── sales_data.csv # Generated sales data ├── code/ # Directory containing code files │ └── forecast_model.py # Python script for developing and validating forecasting models ├── notebooks/ # Directory containing Jupyter notebooks │ └── forecasting_notebook.ipynb # Notebook for time series analysis and model training └── generate_data.py # Script for generating the sales data

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Setup

Follow these steps to set up the project:

1. Clone the Repository

   git clone https://github.com/Seid-M-Adem/swiss-data-science-demos-.git
   cd swiss-data-science-demos-

Create a Virtual Environment (Optional but recommended) bash Copy code python3 -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate Install Dependencies Install the required Python libraries. You can use a requirements.txt file if available, or manually install the dependencies:

bash Copy code pip install pandas numpy scikit-learn matplotlib seaborn Generate the Data Run the generate_data.py script to create the sales_data.csv file:

bash Copy code python generate_data.py Usage

Running the Forecast Model To run the forecasting model and evaluate its performance, use:

bash Copy code python predictive_analytics_forecasting/code/forecast_model.py This script reads the sales data from data/sales_data.csv, trains a Random Forest Regressor model, and evaluates its performance. The script also plots feature importances to help understand the impact of different features on the predictions.

Exploring Data and Training Models To perform exploratory data analysis and model training interactively, launch the Jupyter notebook:

bash Copy code jupyter notebook predictive_analytics_forecasting/notebooks/forecasting_notebook.ipynb In the notebook, you'll find detailed EDA, feature engineering, and model training steps.

Contributions

Contributions to this project are welcome. You can contribute by:

Reporting issues Suggesting improvements Submitting pull requests with bug fixes or new features For major changes or enhancements, please open an issue to discuss your ideas before submitting a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

The data generation script and forecasting model are based on common practices and standard machine learning techniques. Thanks to the open-source community for providing the libraries and tools used in this project. Contact

For any questions or further information, please contact Seid Adem.

Feel free to modify this README as needed to better fit your project's specifics or to add additional information.

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You can copy and paste this README.md content directly into your GitHub repository. It’s organized to provide a comprehensive overview of the project, setup instructions, usage details, and more.