Sumit-Avasthi/Algerian-forest-fires-FWI-Prediction-Model

# 🔥 Algerian Forest Fire Prediction A Machine Learning web application built with **Flask** that predicts the **Fire Weather Index (FWI)** using meteorological and environmental parameters from the Algerian Forest Fires dataset. ## 🚀 Features * Predicts Fire Weather Index using a trained Ridge Regression model * Interactive web interface built with Flask * Responsive input form for user data * Real-time predictions * Deployed on AWS Elastic Beanstalk ## 🛠️ Tech Stack * Python * Flask * Scikit-Learn * NumPy * Pandas * HTML/CSS * AWS Elastic Beanstalk ## 📊 Input Features The model uses the following features: | Feature | Description | | ----------- | ---------------------------- | | Temperature | Temperature in °C | | RH | Relative Humidity | | Ws | Wind Speed | | Rain | Rainfall | | FFMC | Fine Fuel Moisture Code | | DMC | Duff Moisture Code | | ISI | Initial Spread Index | | Classes | Fire/Not Fire Classification | | Region | Geographic Region | ## 📂 Project Structure ```text . ├── application.py ├── requirements.txt ├── models │ ├── ridge.pkl │ └── scaler.pkl ├── templates │ ├── index.html │ └── home.html ├── static │ └── style.css └── README.md ``` ## ⚙️ Installation ### Clone the Repository ```bash git clone github.com cd algerian-forest-fire-prediction ``` ### Create Virtual Environment ```bash python -m venv venv ``` Activate the environment: **Windows** ```bash venv\Scripts\activate ``` **Linux/Mac** ```bash source venv/bin/activate ``` ### Install Dependencies ```bash pip install -r requirements.txt ``` ## ▶️ Run Locally ```bash python application.py ``` Visit: ```text 127.0.0.1 ``` ## 🌐 Deployment This project is deployed using AWS Elastic Beanstalk. Deploy updates using: ```bash …