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---
title: SaleSight – ML model for sales forecasting
emoji: 📈
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: "5.4.0" # 👈 use the latest version
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](./demo/demo.mp4)
## ✨ 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:
```bash
git clone https://github.com/yourusername/sales-forecasting.git
cd sales-forecasting
```
2. Create and activate a virtual environment:
```bash
# 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:
```bash
pip install -r requirements.txt
```
## 🛠️ Usage
1. Run the application:
```bash
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](LICENSE) file for details.
## 🙏 Acknowledgements
- [LightGBM](https://github.com/microsoft/LightGBM) - The gradient boosting framework used for predictions
- [Gradio](https://gradio.app/) - For the simple web interface
- [Pandas](https://pandas.pydata.org/) - For data manipulation and analysis
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