ahmedAmer8/Algerian_Forest_Fire_prediction

# 🔥 Algerian Forest Fire Prediction App This project is a machine learning-based web application for predicting the likelihood of a forest fire in Algeria using weather and fire index data. It utilizes a **Random Forest Classifier** and is deployed via a **Flask web app**. ## 📊 Dataset Overview The dataset includes meteorological and fire weather indices collected from June to September 2012. It contains two classes: - **Fire**: Indicates the presence of a forest fire - **not Fire**: Indicates no fire occurred ### ✅ Features Used | Feature | Description | |--------|-------------| | Date | Day, month, and year (DD/MM/YYYY) | | Temp | Temperature at noon (22°C to 42°C) | | RH | Relative Humidity in % (21% to 90%) | | Ws | Wind speed in km/h (6 to 29) | | Rain | Rainfall in mm (0 to 16.8) | | FFMC | Fine Fuel Moisture Code (28.6 to 92.5) | | DMC | Duff Moisture Code (1.1 to 65.9) | | DC | Drought Code (7 to 220.4) | | ISI | Initial Spread Index (0 to 18.5) | | BUI | Buildup Index (1.1 to 68) | | FWI | Fire Weather Index (0 to 31.1) | --- ## 🧠 Machine Learning Model - **Algorithm:** Random Forest Classifier - **Target variable:** `Classes` (Fire / not Fire) - **Preprocessing:** Feature selection, data cleaning, label encoding - **Model Evaluation:** Accuracy --- ## 🌐 Web App (Flask) The app takes user inputs for all the features and predicts whether a fire is likely to occur.