Miskat362/algerian-forest-fire

## 🔥 Algerian Forest Fire Prediction A machine learning web application to predict the Fire Weather Index (FWI) based on meteorological data from the Algerian forest region. --- This project uses a pre-trained Ridge Regression model to predict forest fire risk. The model is served via a simple web interface built with Flask, allowing users to input weather conditions and receive an instant FWI prediction. ### ✨ Features - **Interactive Web UI:** Easy-to-use web form to input weather data. - **Real-time Prediction:** Get an instant Fire Weather Index (FWI) prediction. - **Machine Learning Model:** Utilizes a Ridge Regression model trained on Algerian forest fire data. - **Ready for Deployment:** The application is configured to be deployed on cloud platforms like AWS Elastic Beanstalk. ### 🛠️ Built With This project is built with the following technologies: - **Backend:** Flask - **ML Library:** Scikit-learn - **Numerical Python:** NumPy & Pandas - **Deployment:** Configured for WSGI servers like Gunicorn. ### 📑 Model Details The prediction model is a **Ridge Regressor** that takes the following 9 features as input: 1. `Temperature`: Temperature in Celsius degrees (22-42) 2. `RH`: Relative Humidity in % (21-90) 3. `Ws`: Wind speed in km/h (6-29) 4. `Rain`: Rain in mm (0-16.8) 5. `FFMC`: Fine Fuel Moisture Code (28.6-92.5) 6. `DMC`: Duff Moisture Code (1.1-65.9) 7. `ISI`: Initial Spread Index (0-18.5) 8. `Classes`: 0 (not fire) or 1 (fire) 9. `Region`: 0 (Sidi-Bel Abbes) or 1 (Bejaia) ### 🤝 Contributing Contributions are welcome! If you have a suggestion that would make this better, please fork the repo and create a pull request. **👥 Connect:** LinkedIn