Ojas025/Algerian-Forest-Fires-Prediction

# **Algerian Forest Fires Prediction App** This is a Flask web application for predicting the Fire Weather Index (FWI) using a trained Ridge Regression model. The model is based on Algerian forest fire data and utilizes standard machine learning tools for preprocessing and prediction. --- ## Features - Input environmental parameters through a web form - Predict Fire Weather Index using a RidgeCV model - Scales data with a pre-trained StandardScaler - Simple and responsive UI with TailwindCSS --- ## Technologies Used - Python 3.x - Flask - scikit-learn - NumPy, Pandas - HTML/CSS (TailwindCSS) - Pickle for model serialization --- ## Project Structure ``` project-root/ │ ├── application.py # Main Flask application ├── models/ │ ├── ridge_cv.pkl # Trained ML model │ └── scaler.pkl # Pre-fitted StandardScaler │ ├── templates/ │ ├── index.html # Landing page │ └── home.html # Form and prediction display │ ├── requirements.txt # Python dependencies └── README.md # Project documentation ``` --- ## Setup Instructions 1. **Clone the repository** ```bash git clone github.com cd algerian-fire-predictor ``` 2. **Create and activate a virtual environment** ```bash python -m venv venv venv\Scripts\activate # On Windows source venv/bin/activate # On macOS/Linux ``` 3. **Install required packages** ```bash pip install -r requirements.txt ``` 4. **Run the application** ```bash python app.py ``` 5. Open your browser and visit: `localhost` --- ## Input Parameters (via Web Form) The following values are required for prediction: * Temperature * Relative Humidity (RH) * Wind Speed (Ws) * Rain * FFMC * DMC * ISI * Classes (binary encoded) * Region (binary encoded) --- ## .gitignore Recommen …