A machine learning web application to predict the Fire Weather Index (FWI) based on meteorological data from two regions in Algeria.

This project is a web-based tool that uses a machine learning model to predict the likelihood of forest fires. It provides a user-friendly interface to input weather conditions and receive a real-time prediction of the Fire Weather Index (FWI), a key indicator of fire risk.

The application is built with a Flask backend to serve a Ridge Regression model and a clean, responsive HTML/CSS front-end for user interaction.

The prediction model was trained on the Algerian Forest Fires Dataset from the UCI Machine Learning Repository.

• Location: The data was collected from two regions in Algeria: the Bejaia region and the Sidi Bel-Abbes region.
• Time Period: The dataset covers the fire season from June 2012 to September 2012.
• Features: It includes daily meteorological observations such as:
  • Temperature
  • Relative Humidity (RH)
  • Wind Speed (Ws)
  • Rain
  • And various components of the Canadian Forest Fire Weather Index (FWI) System (FFMC, DMC, ISI).
• Target: The model is trained to predict the Fire Weather Index (FWI), which is a numerical rating of fire intensity.

• Interactive Prediction Form: Users can easily input values for temperature, humidity, wind speed, and other factors.
• Real-time Predictions: The backend model processes the inputs and instantly returns the predicted FWI value.
• Responsive Design: The user interface is designed to work seamlessly on both desktop and mobile devices.
• Two Algerian Regions: The model accounts for data from both the Bejaia and Sidi-Bel Abbes regions.

• Backend: Python, Flask
• Machine Learning: Scikit-learn, Pandas, NumPy
• Frontend: HTML, CSS
• Deployment: Render, Gunicorn

To set up and run this project on your local machine, follow these steps:

1. Clone the repository:

   git clone <your-repository-url>
   cd <your-repository-directory>

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

4. Run the application:

   python application.py

5. Open your web browser and go to http://127.0.0.1:8080.

This application is deployed on Render using a Procfile to run a Gunicorn web server. Continuous deployment is set up to automatically update the live application whenever new changes are pushed to the main branch on GitHub.

• This project uses the Algerian Forest Fires Dataset from the UCI Machine Learning Repository.