VenkataViswas/Algeria-FireWatch-Real-time-Risk-Estimator

# Algeria FireWatch - Real time Risk Estimator 🌲🔥 ## ✅ Project Overview This project predicts the *Fire Weather Index (FWI)*, a proxy for fire risk, using meteorological and environmental data across two Algerian regions: Bejaia and Sidi Bel Abbes. A machine learning model (Ridge) powered by scikit-learn is wrapped in a Flask web app and deployed AWS EC2. --- ## 📁 Dataset - **Source**: Algerian Forest Fires dataset - **Timeframe**: June–September 2012 - **Regions**: Bejaia (northeast) & Sidi Bel Abbes (northwest) - **Total instances**: 244 (122 per region) ### Features: - **Date** - **Meteorological**: - Temperature (°C) - Relative Humidity (%) - Wind Speed (km/h) - Rain (mm) - **FWI Components**: - FFMC - DMC - DC - ISI - BUI - **Target**: Fire Weather Index (FWI) ### Ranges: | Feature | Min | Max | |--------|------|------| | Temp | 22 °C | 42 °C | | RH | 21 % | 90 % | | Ws | 6 km/h | 29 km/h | | Rain | 0 mm | 16.8 mm | | FFMC | 28.6 | 92.5 | | DMC | 1.1 | 65.9 | | DC | 7 | 220.4 | | ISI | 0 | 18.5 | | BUI | 1.1 | 68.0 | | FWI | 0 | 31.1 | --- ## 🔍 Model Development - **Preprocessing**: - Data cleaning - Type conversion - Scaling using `StandardScaler` - Region encoding - **Algorithm**: - `Ridge Linear Regression` (L2 regularization) - **Performance**: - **R² score** ≈ 0.98 - **Mean Absolute Error (MAE)**: ~0.56 --- ## 🏗️ Project Structure ``` algerian_forest_fire_predictor/ ├── app.py # Flask application entrypoint ├── models/ │ └── ridge.pkl # Serialized Ridge Linear Regression model └── Scalare.pkl # Serialized Stadard Scalar Model ├── static/ # CSS/JS files ├── templates/ # HTML templates (index, result views) ├── notebooks/ │ ├── …_EDA.ipynb # Exploratory Data Analysis │ └── …_Model_training.ipynb # Feature engineering & model training ├── requirements.txt # Dependencies └── README.md # Project doc …