krish231752/Algerian-Forest-Fire-Prediction

# 🔥 Algerian Forest Fire Prediction A Machine Learning-powered web application that predicts the **Fire Weather Index (FWI)** using meteorological data from the Algerian Forest Fires dataset. The application is built with **Flask** and deployed online for real-time predictions. 🌐 **Live Demo:** algerian-forest-fire-predic… ## 📌Project Overview Forest fires are one of the most destructive natural disasters. Predicting fire weather conditions can help authorities take preventive measures and reduce damage. This application uses a **Ridge Regression** model trained on the **Algerian Forest Fires Dataset** to estimate the **Fire Weather Index (FWI)** based on weather conditions. ## What is Fire Weather Index (FWI)? The Fire Weather Index (FWI) is a numerical indicator used to estimate the potential intensity and spread of forest fires based on weather conditions. Higher FWI values indicate more favorable conditions for wildfire ignition and rapid spread. | FWI Range | Risk Level | | ------------ | ---------------- | | **0 – 5** | 🟢 Very Low Risk | | **5 – 15** | 🟡 Low Risk | | **15 – 30** | 🟠 Moderate Risk | | **Above 30** | 🔴 High Risk | ## 🚀 **Features** - Predicts Fire Weather Index (FWI) - Interactive Flask Web Interface - Real-time predictions - Risk classification - Error handling - Responsive UI - Cloud deployment using Render ## 📊 **Machine Learning Model** ### Algorithm Used - Ridge Regression ### Why Ridge Regression? - Reduces overfitting using L2 Regularization - Performs well with correlated features - Produces stable predictions - Suitable for continuous value prediction (FWI) ## 📂 Input Features | Feature | Description | | -------------------------- | --------------------------------------------------------------------------------- | | …