raw9k/Algerian-Forest-Fire-Linear-Regression-Project

This project uses machine learning to predict the Fire Weather Index (FWI) in Algerian forests based on meteorological and environmental features. Built with Flask, it provides a user-friendly web interface for making real-time predictions. # 🔥 Forest Fire Prediction - Algeria This project uses machine learning to predict the Fire Weather Index (FWI) in Algerian forests based on meteorological and environmental features. Built with Flask, it provides a user-friendly web interface for making real-time predictions. ## 🧠 Project Overview Forest fires can have devastating ecological and economic impacts. This web application leverages Ridge Regression to predict the likelihood of fire occurrence using key indicators such as temperature, humidity, wind speed, and others. The app is designed to assist in fire prevention and response planning. ## 🚀 Features - 🔍 Real-time FWI prediction using environmental parameters - 📈 Machine learning model trained on the Algerian Forest Fires Dataset - 📊 Data visualization and preprocessing using pandas and matplotlib - 🧪 Model built using scikit-learn with standardized scaling - 🌐 Web application using Flask and HTML/CSS interface ## 🧰 Tech Stack - Python (3.x) - Flask (Backend Web Framework) - HTML/CSS (Frontend) - scikit-learn (Machine Learning) - Pandas, NumPy (Data Processing) - Pickle (Model Serialization) ## 🧪 Usage - Go to the home page and fill in the input form with environmental data. - Click Predict. - The model will return the Fire Weather Index value based on input. ### 📌 Note This project is built for educational purposes and is not intended for deployment in critical systems. Always verify predictions with domain experts.