npratik01/Algerian-Forest-Fire-Prediction

# Algerian Forest Fire Prediction A machine learning web application that predicts Fire Weather Index (FWI) for Algerian forest fires using meteorological data. ## Features - **Modern UI**: Beautiful, responsive design with glassmorphism effects - **Real-time Predictions**: Instant fire risk assessment - **Interactive Form**: User-friendly input form with tooltips and validation - **Risk Classification**: Color-coded risk levels (Low, Moderate, High, Very High) - **Mobile Responsive**: Works seamlessly on all devices ## Technology Stack - **Backend**: Flask (Python) - **Frontend**: HTML5, CSS3, JavaScript - **Machine Learning**: Scikit-learn (Ridge Regression) - **Deployment**: Vercel ## Input Parameters - **Temperature**: Air temperature in degrees Celsius - **RH**: Relative humidity percentage - **Ws**: Wind speed in km/h - **Rain**: Rainfall in millimeters - **FFMC**: Fine Fuel Moisture Code - **DMC**: Duff Moisture Code - **ISI**: Initial Spread Index - **Classes**: Fire danger class - **Region**: Region identifier ## Local Development 1. Clone the repository 2. Install dependencies: `pip install -r requirements.txt` 3. Run the application: `python application.py` 4. Open localhost in your browser ## Deployment This application is configured for deployment on Vercel with the following files: - `vercel.json`: Vercel configuration - `index.py`: Entry point for Vercel - `runtime.txt`: Python version specification - `.vercelignore`: Files to exclude from deployment ## Live Demo Deploy your own copy to Vercel