ShubhamMaurya2001/algeria-forest-fire-prediction

# ML Lifecycle - Algerian Forest Fire Prediction A machine learning web application for predicting Forest Fire Weather Index (FWI) using ridge regression and providing SHAP-based model explainability. 🌐 **Live Demo** - Try the app online now! 📦 **GitHub**: algeria-forest-fire-prediction ## Features - **Real-time Prediction**: Predict FWI values based on 9 input features (Temperature, RH, Ws, Rain, FFMC, DMC, DC, ISI, BUI) - **SHAP Explainability**: Generate feature importance charts to understand model predictions - **Interactive Web UI**: User-friendly interface built with Streamlit - **Pre-trained Model**: Ridge regression model trained on Algerian forest fires dataset - **Data Visualization**: Explore correlations and feature distributions ## Project Structure ``` ├── streamlit_app.py # Main Streamlit application ├── application.py # Flask app (alternative) ├── explainability.py # SHAP computation utilities ├── ridge_and_lasso_regression.ipynb # Model training & analysis notebook ├── Templates/ # Flask templates (optional) ├── Models/ │ ├── ridge_model.pkl # Pre-trained ridge regression model │ └── scaler.pkl # StandardScaler for feature normalization ├── Algerian_forest_fires_cleaned.csv # Dataset used for training ├── requirements.txt # Python dependencies └── README.md # This file ``` ## Installation ### Prerequisites - Python 3.10 or higher - pip or conda package manager ### Quick Start (5 minutes) 1. **Clone the repository**: ```bash git clone github.com cd algeria-forest-fire-prediction ``` 2. **Install dependencies**: ```bash pip install -r requirements.txt ``` 3. **Run the Streamlit application**: ```bash streamlit run streamlit_app.py ``` 4. **Open in browser**: ``` localhost ``` **That's it!** Yo …