Rithi123456/Algerian_forest_fire_Prediction

# Algerian Forest Fire Prediction System ## Project Overview The **Algerian Forest Fire Prediction System** is an end-to-end Machine Learning web application designed to predict the **Fire Weather Index (FWI)** using meteorological and environmental parameters. This project combines **Machine Learning, Data Analytics, Flask Web Development, Docker, Jenkins, and AWS Deployment** to build a complete forest fire risk intelligence system. The application allows users to: - Predict forest fire risk based on environmental inputs - Analyze fire risk using a trained machine learning model - Visualize prediction factors using interactive analytics charts - Explore historical dataset insights through dashboard analytics - Deploy the application using Docker and Jenkins CI/CD --- ## Features ### Forest Fire Prediction Users can input environmental factors such as: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Initial Spread Index (ISI) - Fire Class - Region The system predicts: - **Fire Weather Index (FWI)** Risk Level Classification: - Low Risk - Moderate Risk - High Risk - Extreme Risk --- ### Analytics Dashboard The analytics dashboard provides: - Latest prediction analysis - Prediction factor visualization using Chart.js - Historical dataset insights - Fire vs Non-Fire distribution - Region-wise fire occurrence analysis --- ## Dataset Description The project uses the **Algerian Forest Fires Dataset**, containing weather and fire-related measurements from two Algerian regions: - Bejaia - Sidi-Bel Abbes ### Features - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - ISI - Classes - Region ### Target Variable - Fire Weather Index (FWI) --- ## Machine Learning Workflow ### Data Preprocessing - Dataset cleaning - Null value handling - Feature selection …