jahanvigarg238/Algerian_forest_fire_Prediction

# 🔥 Algerian Forest Fire — FWI Predictor (Flask App) End-to-end deployment of the Ridge Regression model trained on the Algerian Forest Fires dataset. ## Project Structure ``` flask_regression_app/ ├── app.py # Flask application (routes + API) ├── requirements.txt ├── models/ │ ├── ridge.pkl # Trained Ridge Regression model │ ├── scaler.pkl # Fitted StandardScaler │ └── dataset.csv # Original dataset (for EDA charts) └── templates/ └── index.html # Full interactive frontend ``` ## Setup & Run ```bash # 1. Create virtual environment python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate # 2. Install dependencies pip install -r requirements.txt # 3. Run the app python app.py ``` Open localhost in your browser. ## Features | Section | Description | |---------|-------------| | **Predict** | Enter 9 weather/FWI inputs, get FWI prediction + risk label | | **EDA** | 4 interactive charts: histogram, monthly trend, scatter, class dist | | **Model** | Ridge coefficients, feature importances, model metadata | ## Input Features | Feature | Description | Range | |---------|-------------|-------| | Temperature | Max temperature at noon (°C) | 22–42 | | RH | Relative Humidity (%) | 21–90 | | Ws | Wind Speed (km/h) | 6–29 | | Rain | Total rain (mm) | 0–16.8 | | FFMC | Fine Fuel Moisture Code | 28.6–92.5 | | DMC | Duff Moisture Code | 1.1–65.9 | | ISI | Initial Spread Index | 0–18.5 | | Classes | Fire or Not Fire | 0 / 1 | | Region | Bejaia or Sidi-Bel Abbes | 0 / 1 | ## API Endpoints ``` GET / → Main UI POST /predict → JSON prediction { fwi, risk_label, risk_color } GET /api/eda → EDA chart data GET /api/model_info → Coefficients + model metadata ``` ## FWI Risk Scale | FWI Range | Risk Level | |-----------|------------| | 0–5 | Very Low | | 5–10 | Low | | 10–17 | Moderate | | 17–24 | Hig …