tejask0512/Algerian_ForestFire_ML-project_Cloud

# Algerian Forest Fire Prediction A machine learning web application that predicts the burned area of forest fires in Algeria using meteorological and geographical data. ## Project Overview This project implements a Ridge Regression model to predict the extent of burned areas in forest fires based on various environmental factors. The model is deployed as a web application using Flask, allowing users to input parameters and receive predictions in real-time. ## Dataset The model was trained on the Algerian Forest Fire dataset, which contains weather data and fire observations from the Sidi Bel-abbes region and Bejaia region of Algeria from June 2012 to September 2012. Key Features: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rainfall (Rain) - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Initial Spread Index (ISI) - Classes (numerical encoding of fire danger) - Region (numerical encoding of geographical region) Target Variable: - Burned Area (in hectares) ## Technologies Used - **Python**: Core programming language - **Flask**: Web framework - **Scikit-learn**: Machine learning library - Ridge Regression - Standard Scaler - **Pandas & NumPy**: Data manipulation and numerical operations - **Pickle**: Model serialization - **HTML/CSS**: Frontend interface ## Project Structure ``` ├── app.py # Flask application ├── config.py # Configuration settings ├── models/ # Trained model files │ ├── ridge.pkl # Ridge regression model │ ├── scaler.pkl # StandardScaler preprocessing model │ └── linear.pkl # Linear regression model (alternative) ├── templates/ # HTML templates │ ├── index.html # Landing page │ └── home.html # Results page ├── README.md # Project documentation └── requirements.txt # Dependencies ``` ## Installation & Setup 1. Clone the repository: ``` git clone github.com …