PRIYANSUPAT/Algerian-Forest-Fire-Prediction

# Algerian-Forest-Fire-Prediction# Algerian Forest Fire Prediction 🔥 ## Project Overview This project predicts the **Fire Weather Index (FWI)** using weather and environmental features from the **Algerian Forest Fire Dataset**. A **Machine Learning model (Ridge Regression)** is trained and deployed using **Flask** to allow users to input environmental data and receive fire risk predictions. --- ## Dataset The dataset contains **weather and fuel information** collected from two regions in Algeria. Features used: * Temperature * Relative Humidity (RH) * Wind Speed (Ws) * Rain * FFMC * DMC * ISI * Classes * Region Target variable: * **FWI (Fire Weather Index)** --- ## Technologies Used * Python * Scikit-learn * NumPy * Pandas * Flask * HTML / CSS --- ## Project Structure ``` machine_learning_project │ ├── dataset ├── models │ ├── scaler.pkl │ └── ridge.pkl ├── notebook ├── templates │ └── home.html ├── app.py ├── requirement.txt └── README.md ``` --- ## How to Run the Project 1. Clone the repository ``` git clone ``` 2. Install dependencies ``` pip install -r requirement.txt ``` 3. Run the Flask application ``` python app.py ``` 4. Open browser ``` 127.0.0.1 ``` --- ## Output The system predicts: * **FWI Value** * **Fire Risk Level (Low / Moderate / High / Extreme)** --- ## Future Improvements * Use better models (Random Forest, XGBoost) * Improve UI * Deploy on cloud (Render / AWS / Heroku)