GouravAgarwal2716/algerian-forest-fire-prediction

Flask web app for predicting Algerian forest fire weather index using machine learning # Flask ML Deployment - Algerian Forest Fires Prediction A Flask web application to deploy a machine learning model for predicting Algerian forest fire weather indices. ## Project Structure ``` Flask_App/ ├── app.py # Main Flask application ├── config.py # Configuration settings ├── export_model.py # Script to export trained models ├── requirements.txt # Python dependencies ├── Procfile # Heroku deployment configuration ├── .gitignore # Git ignore file ├── templates/ │ └── index.html # Main web interface ├── static/ │ └── css/ │ └── style.css # Styling └── models/ # Directory for trained models (*.pkl files) ├── scaler.pkl # Fitted StandardScaler └── lasso_cv_model.pkl # Trained LassoCV model ``` ## Features - **Web Interface**: User-friendly form to input forest fire features - **Real-time Predictions**: Get FWI (Fire Weather Index) predictions instantly - **API Endpoints**: RESTful API for programmatic predictions - **Model Deployment**: Ready for Heroku, Docker, or other cloud platforms ## Installation ### 1. Create Virtual Environment ```bash # Windows python -m venv venv venv\Scripts\activate # macOS/Linux python3 -m venv venv source venv/bin/activate ``` ### 2. Install Dependencies ```bash pip install -r requirements.txt ``` ### 3. Prepare Models First, train your model in the Jupyter notebook and export it: ```python # In your notebook after training from export_model import export_models export_models(lasso_cv, scaler) ``` This will create: - `models/scaler.pkl` - Fitted StandardScaler - `models/lasso_cv_model.pkl` - Trained model ## Running the Application ### Local Development ```bash python app.py ``` Visit: `localhost` ### Production (Heroku) ```bash # Install Heroku CLI # Login to Heroku heroku login # Create app heroku create your-app-name # Deploy git push heroku main # View logs heroku logs --tail ``` ### D …