kislay2702/Algerian-Forest-Fire-Prediction-

# 🔥 Algerian Forest Fire Prediction ## 📌 Project Overview This project predicts the **Fire Weather Index (FWI)** using Machine Learning techniques and Flask web deployment. The application takes weather and fire-related parameters from users through a web interface and predicts the FWI value using a trained **Ridge Regression** model. --- # 🚀 Features * Machine Learning Regression Model * Flask Web Application * User Input Form * Real-time Prediction * Model Deployment Ready * Git & GitHub Integration --- # 🛠️ Technologies Used * Python * Flask * Scikit-learn * Ridge Regression * NumPy * Pandas * HTML/CSS * Bootstrap * Git & GitHub --- # 📂 Dataset The project uses the **Algerian Forest Fire Dataset** containing weather and fire-related attributes collected from two Algerian regions. --- # 📊 Input Features The model uses the following features for prediction: * Temperature * RH (Relative Humidity) * Ws (Wind Speed) * Rain * FFMC * DMC * ISI * Classes * Region --- # 🤖 Machine Learning Model ### Model Used: * Ridge Regression ### Workflow: 1. Data Cleaning 2. Exploratory Data Analysis (EDA) 3. Feature Engineering 4. Feature Scaling 5. Model Training 6. Model Serialization using Pickle 7. Flask Deployment --- # 📁 Project Structure ```text id="mjlwm1" forest-fire-prediction/ │ ├── application.py ├── ridge.pkl ├── scaler.pkl ├── requirements.txt ├── Procfile │ ├── templates/ │ └── index.html │ └── README.md ``` # ⚙️ Installation & Setup ## Clone Repository ```bash id="mjlwm2" git clone github.com ``` ## Move to Project Folder ```bash id="mjlwm3" cd forest-fire-prediction ``` ## Install Dependencies ```bash id="mjlwm4" pip install -r requirements.txt ``` ## Run Flask Application ```bash id="mjlwm5" python application.py ``` --- # 🌐 Application Demo The web application accepts input parameters and predicts the Fire Weather Index (FWI) value instantly. --- # 📈 Future Improvements * Improv …