Emmacris1/Algerian_Forest_Fire

ML web app using Streamlit predicts forest fire risk via meteorological & fire index features (RH, WS, FFMC, DMC, ISI, FWI, etc.). Trained with Random Forest algorithm, deployed for interactive real-time predictions to support fire prevention efforts. Algerian Forest Fire Prediction App 📌 Project Overview This project is a Machine Learning web application that predicts the likelihood of forest fire occurrence using meteorological and fire index data. The model was trained using a Random Forest algorithm and deployed using Streamlit to allow real-time user interaction. The goal of this project is to demonstrate an end-to-end ML workflow — from model training and serialization to deployment as a functional web application. 🚀 Live Application The app allows users to input environmental and fire-related parameters and instantly receive predictions. Input Features: Relative Humidity (RH) Wind Speed (WS) Rain FFMC (Fine Fuel Moisture Code) DMC (Duff Moisture Code) ISI (Initial Spread Index) DC (Drought Code) FWI (Fire Weather Index) BUI (Build-Up Index) Region Class 🧠 Machine Learning Model Algorithm: Random Forest Classifier Model serialized using joblib Model file: rand_model.pkl Real-time inference integrated into a Streamlit interface 🛠 Tech Stack Python Pandas NumPy Scikit-learn Joblib Streamlit