Shreya7078/Algerian-Forest-Fire-Project

# Forest Fire Weather Index (FWI) Prediction ## Overview Machine learning model to predict Forest Fire Weather Index using Ridge Regression with Flask web interface. ## Project Structure RidgeLassoElasticNet/ ├── models/ # Trained models (ridge.pkl, scaler.pkl) ├── notebooks/ # Jupyter notebooks and datasets ├── templates/ # HTML templates ├── application.py # Flask web app └── Requirements.txt # Dependencies ## Features - Input: Temperature, RH, Wind Speed, Rain, FFMC, DMC, ISI, Classes, Region - Output: FWI prediction - Model: Ridge Regression with StandardScaler - Web Interface: Flask application ## Quick Start 1. Install dependencies: bash pip install -r Requirements.txt 2. Run application: bash python application.py 3. Access: Open browser → localhost ## Usage 1. Fill weather parameters in the form 2. Click "Predict" 3. Get FWI prediction result ### Example Input: Temperature: 29, RH: 57, Ws: 18, Rain: 0.0 FFMC: 65.7, DMC: 3.4, ISI: 1.3 Classes: 0, Region: 0 ## Model Details - Algorithm: Ridge Regression (L2 regularization) - Preprocessing: Removed multicollinear features (BUI, DC) - Scaling: StandardScaler normalization - Dataset: Algerian Forest Fires data ## Files - ModelTraining.ipynb: Complete ML pipeline - application.py: Flask web server - models/: Saved model and scaler - templates/: HTML interface