Somojeet123/Algerian_forest

🌲 Algerian Forest Fire Prediction – ML Web App A production-ready Machine Learning web application that predicts the Fire Weather Index (FWI) using meteorological and fire weather index parameters from Algerian forest data. The application is built using Flask and deployed on Render for scalable cloud-based inference. 🚀 Live Application Live URL: algerian-forest-1.onrender.… 🧠 Problem Statement Forest fires cause severe environmental and economic damage. Early prediction of fire risk helps authorities take preventive measures and manage resources effectively. This project predicts the Fire Weather Index (FWI) using meteorological conditions and fire weather indices from Algerian forest regions. The system allows users to input environmental parameters and instantly receive predictions through a web interface. 📊 Dataset Dataset Name: Algerian Forest Fires Dataset Regions Covered: Bejaia Sidi Bel-Abbes The dataset contains meteorological observations and fire weather indices collected from Algerian forests. 🔢 Input Features Temperature Relative Humidity (RH) Wind Speed (Ws) Rain FFMC (Fine Fuel Moisture Code) DMC (Duff Moisture Code) ISI (Initial Spread Index) Classes (Fire / No Fire) Region (Bejaia / Sidi Bel-Abbes) 🛠️ Tech Stack Machine Learning Python 3.10 NumPy Pandas Scikit-learn Ridge Regression Web Development Flask HTML CSS Deployment Gunicorn Render ☁️ Cloud Deployment Application deployed on Render as a Web Service Gunicorn used as production WSGI server Python runtime environment used for hosting Real-time prediction system for machine learning inference 📈 Model Details Algorithm: Ridge Regression Preprocessing: Standard Scaling Python Version: 3.10 Model Persistence: Pickle (.pkl files) ✅ Key Learnings End-to-end machine learning project workflow Data preprocessing and feature engineering Model training and evaluation Model serialization usi …