README.md

metadata

title: SaleSight – ML model for sales forecasting
emoji: 📈
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 5.4.0
app_file: app.py
pinned: false

Sales Forecasting with LightGBM

A retail sales prediction application built with LightGBM and Gradio for interactive forecasting.

📊 Demo

Watch Demo Video

✨ Features

• Interactive web interface for sales prediction
• Takes into account various features including:
  • Promotional events
  • Holiday status
  • Historical sales data (various lags and rolling means)
  • Temporal features (day, month, year, day of week)
• Built with LightGBM for fast and accurate predictions
• Simple and intuitive user interface

🚀 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sales-forecasting.git
   cd sales-forecasting
   

2. Create and activate a virtual environment:

   # Create a virtual environment
   python -m venv .venv
   
   # Activate it
   # On Linux/Mac:
   source .venv/bin/activate
   # On Windows:
   .venv\Scripts\activate
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

🛠️ Usage

1. Run the application:

   python app.py
   

2. Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:7860)

3. Input the required information:

   • Promo status (0 or 1)
   • Holiday status (0 or 1)
   • Date in YYYY-MM-DD format
   • Sales lags and rolling means

4. Click "Predict Sales" to see the prediction

📦 Dependencies

• gradio >= 3.50.0
• joblib >= 1.3.0
• lightgbm >= 4.0.0
• pandas >= 2.0.0

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• LightGBM - The gradient boosting framework used for predictions
• Gradio - For the simple web interface
• Pandas - For data manipulation and analysis