Adarsh282504/Algerian_Forest_Fire_Prediction

Machine Learning Web Application for Fire Weather Index Prediction using Ridge Regression # 🔥 Algerian Forest Fire Prediction A Machine Learning web application that predicts the Fire Weather Index (FWI) for the Algerian Forest Fire dataset using Ridge Regression. The application is built with Flask and deployed on Render. ## 🌐 Live Demo algerian-forest-fire-predic… --- ## 📌 Project Overview Forest fires pose a significant environmental and economic threat. This project leverages Machine Learning to predict the Fire Weather Index (FWI), helping estimate fire risk based on weather conditions. The model is trained on the Algerian Forest Fire Dataset and deployed as a responsive web application. --- ## 🚀 Features - Predicts Fire Weather Index (FWI) - Interactive Flask Web Interface - Responsive UI - Input Validation - Risk Level Classification - Deployed on Render - Fast Predictions using a trained Ridge Regression model --- ## 📸 Application Preview --- ## 🧠 Machine Learning Workflow 1. Data Collection 2. Data Cleaning 3. Exploratory Data Analysis (EDA) 4. Feature Engineering 5. Data Scaling 6. Model Training 7. Model Evaluation 8. Model Deployment using Flask & Render --- ## 📊 Input Features - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - ISI - Classes - Region --- ## 🤖 Model Used - Ridge Regression The model was selected after comparing multiple regression algorithms and provided strong predictive performance for this dataset. --- ## 🛠 Tech Stack - Python - Flask - Scikit-learn - NumPy - HTML - CSS - JavaScript - Gunicorn - Render --- ## 📂 Project Structure ``` Algerian_Forest_Fire_Prediction/ │ ├── app.py ├── ridge.pkl ├── scaler.pkl ├── requirements.txt ├── runtime.txt ├── Procfile ├── templates/ │ └── index.html ├── static/ │ ├── style.css │ └── script.js └── README.md ``` --- ## ⚙️ Installation Clone the repository ```bash git clone github.com ``` Move into the project ```bash cd Algerian_Forest …