akshatkh18/TestForestFire

This code file gives the prediction of the FWI(Fire Weather Index). Model is trained on the Algerian Forest Fire dataset. # 🌲 Forest Fire Prediction This project predicts the likelihood of forest fires using machine learning models. It takes input features (like temperature, humidity, wind, etc.) and outputs whether a fire is likely to occur. The project also includes a simple web interface built with **Flask**. --- ## 🚀 Features - Pre-trained ML model for fire prediction. - Flask web app with user-friendly forms. - Input standardization before prediction. - HTML templates (`home.html`, `index.html`) for UI. - Modular project structure. --- ## 📂 Project Structure ``` Algerian_Forest_Fire/ │-- models/ # Contains .pkl model files │-- templates/ # HTML files (home.html, index.html) │-- static/ # (optional) CSS/JS files │-- app.py # Main Flask application │-- requirements.txt # Dependencies │-- README.md # Project documentation ``` --- ## 🛠 Installation & Setup ### 1. Clone the repository ```bash git clone github.com cd TestForestFire ```` ### 2. Create and activate virtual environment (recommended) ```bash python -m venv venv # Windows venv\Scripts\activate # macOS/Linux source venv/bin/activate ``` ### 3. Install dependencies ```bash pip install -r requirements.txt ``` ### 4. Run the Flask app ```bash python app.py ``` Now open your browser and visit **127.0.0.1 --- ## 📦 Requirements * Python 3.8+ * Flask * Scikit-learn * Pandas * Numpy (Install all via `requirements.txt`) --- ## 🔮 Future Improvements * Deploy on **Render/Heroku/Streamlit** for public access. * Add **real-time weather API integration**. * Improve UI design. --- ## 🤝 Contributing Pull requests are welcome! For major changes, please open an issue first to discuss what you’d like to change. --- ## 📜 License This project is licensed under the MIT License. ---