sid090704/Algerian-Forest-Fire-Prediction

This project is a machine learning application designed to predict the Fire Weather Index (FWI), a key indicator of forest fire risk, based on specific weather data from Algeria. The goal is to provide a tool that can help in anticipating and managing forest fires by understanding the relationship between weather conditions and fire probability. # 🔥 Algerian Forest Fire Prediction 🔥 A machine learning web application to predict the Fire Weather Index (FWI) based on meteorological data from two regions in Algeria. --- ## 📝 Overview This project is a web-based tool that uses a machine learning model to predict the likelihood of forest fires. It provides a user-friendly interface to input weather conditions and receive a real-time prediction of the Fire Weather Index (FWI), a key indicator of fire risk. The application is built with a Flask backend to serve a Ridge Regression model and a clean, responsive HTML/CSS front-end for user interaction. --- ## 📊 About the Dataset The prediction model was trained on the **Algerian Forest Fires Dataset** from the UCI Machine Learning Repository. - **Location:** The data was collected from two regions in Algeria: the Bejaia region and the Sidi Bel-Abbes region. - **Time Period:** The dataset covers the fire season from June 2012 to September 2012. - **Features:** It includes daily meteorological observations such as: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - And various components of the Canadian Forest Fire Weather Index (FWI) System (FFMC, DMC, ISI). - **Target:** The model is trained to predict the **Fire Weather Index (FWI)**, which is a numerical rating of fire intensity. --- ## ✨ Features - **Interactive Prediction Form:** Users can easily input values for temperature, humidity, wind speed, and other factors. - **Real-time Predictions:** The backend model processes the inputs and instantly returns the predicted FWI value. - **Responsive Design:** The user interface is designed to work seamlessly on both desktop and mobile devices. - **Two Algerian Regions:** The model accounts for data from both the Bejaia and Sidi-Bel Abbes regions. --- ## 🛠️ Technology Stack - **Backend:** Python, Flask - **Machine Learning:** Scikit-learn, Pandas, NumPy - **Frontend:** HTML, CSS - **Deployment:** Render, …