Aashutosh029/Algerian-Forest-Fire-Prediction

# Forest Fire FWI Prediction Web App A Flask-based web application for predicting the Fire Weather Index (FWI) using a trained Ridge Regression model. The app accepts nine meteorological and fire data inputs, scales them with a saved standard scaler, and returns a predicted FWI value. ## 🚀 Project Overview This project is built to demonstrate a machine learning deployment as a web app. It uses: - `Flask` for the web interface - `scikit-learn` for the saved Ridge Regression model - `numpy` for input conversion and prediction handling - `Bootstrap` and custom CSS for a modern UI ## 📁 Project Structure ```text Project-One/ │ ├── app.py # Flask application entry point ├── README.md # Project documentation ├── requirements.txt # Python dependencies ├── model/ │ ├── ridge.pkl # Trained Ridge Regression model │ └── scaler.pkl # Saved StandardScaler for input scaling └── templates/ └── index.html # HTML template for the web app ``` ## ✅ Features - Modern Flask web interface - Responsive input form - Model explanation cards at the bottom - Input validation for numeric values - Prediction display with polished styling - Works with saved scaler and model objects ## 💻 Requirements - Python 3.10+ recommended - `Flask` - `numpy` - `scikit-learn` ## 📦 Installation 1. Clone the repository to your local machine. 2. Create and activate a virtual environment: ```bash python -m venv venv .\venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` ## ▶️ Run the App From the project root, run: ```bash python app.py ``` Then open the app in your browser at: ```text 127.0.0.1 ``` ## 🧪 Test Example Try these sample values: - `Temperature`: 30 - `RH`: 50 - `Ws`: 10 - `Rain`: 0 - `FFMC`: 80 - `DMC`: 20 - `ISI`: 5 - `Classes`: 0 - `Region`: 0 Another test example: - `Temperature`: 35 - `RH`: 30 - `Ws`: 15 - `Rain`: 0 - `FFMC`: 90 - `DMC`: 30 - `ISI`: 10 - `C …