Amar-techh/Algerian-Forest-Fire-Predictor

# Algerian Forest Fire - Fire Weather Index (FWI) Predictor A modern, full-stack Machine Learning web application that utilizes an optimized Ridge Regression architecture to predict the Fire Weather Index (FWI) for the Algerian forest region based on real-time meteorological metrics. ## 🚀 Live Interface Preview The application features a sleek, responsive glassmorphic dashboard built using Tailwind CSS for streamlined metric inputs and real-time inference generation. ## 🛠️ Tech Stack - **Backend Framework:** Flask (Python) - **Machine Learning Architecture:** Ridge Regression (Scikit-Learn) - **Data Engineering:** Pandas, NumPy, StandardScaler (Pickle Serialization) - **User Interface:** HTML5, Tailwind CSS via CDN ## 📊 Dataset & Features The model evaluates a 9-factor multi-variate matrix to compute systemic fire risk constraints: 1. **Temperature (°C)** - Ambient atmospheric temperature 2. **Relative Humidity (%)** - Relative air moisture constraints 3. **Wind Speed (km/h)** - Atmospheric air velocity vectors 4. **Rainfall (mm)** - Real-time precipitation volume 5. **FFMC Index** - Fine Fuel Moisture Code 6. **DMC Index** - Duff Moisture Code 7. **ISI Index** - Initial Spread Index 8. **Classes** - Spatial fire indicator constraints 9. **Region** - Demarcated regional zone parameters --- ## 💻 How to Run This Project Locally ### 1. Clone the repository ```bash git clone github.com cd Algerian-Forest-Fire-Predictor