mShubham18/Algerian_Fire_prediction

# Algerian Fire Prediction ## Project Overview The **Algerian Fire Prediction** project is a Flask-based web application that predicts the Forest Fire Weather Index (FWI) in Algeria based on various environmental factors. Using a pre-trained RidgeCV regression model, the app processes user-provided inputs and generates predictions on the likelihood of fire hazards in a given region. The goal is to provide a simple, interactive interface to predict fire risks in real time. --- ## Features - **Dynamic Form-Based Input**: Users can input features such as: `Temperature`, `Relative Humidity (RH)`, `Wind Speed (Ws)`, `Rain`, `Fine Fuel Moisture Code (FFMC)`, `Duff Moisture Code (DMC)`, `Initial Spread Index (ISI)`, `Classes`, and `Region`. - **Real-Time Prediction**: The app provides the predicted Forest Fire Weather Index (FWI) instantly based on the input values. - **Model Integration**: Uses a pre-trained **RidgeCV regression model** to make predictions. - **Interactive Web Interface**: Built with **Flask** for a smooth and intuitive user experience. --- ## Tech Stack - **Backend**: Python (Flask) - **Machine Learning**: Scikit-learn (RidgeCV Regression) - **Frontend**: HTML, CSS - **Deployment**: Flask Development Server --- ## Prerequisites Before running the project, ensure the following are installed: - scikit-learn - pandas - ipykernel - numpy - matplotlib - pandas - seaborn - requests - bs4 - Flask - imblearn - openpyxl - statsmodels ## Installation and Setup 1. **Clone the repository**: ```bash git clone github.com cd Algerian_Fire_prediction 2. **Install the required modules:** ```bash pip install -r requirements.txt 3. **Run the Flask app:** ```bash python app.py 4. **Workflow** - **Screenshot :** - **Screen Recording :** ## Example Input and Output ### Example Input: Temperature: 30 RH: 45 Ws: 6 Rain: 0.1 FFMC: 85.0 DMC: 35.0 ISI: 10.0 Classes: 1 Region: 2 ### Example Output: The predicted FW …