Deepicasree/Algerian-Forest-Fire-Prediction

# 🔥 Algerian Forest Fire Prediction using Machine Learning ## 📌 Project Overview This project predicts the **Fire Weather Index (FWI)** using a **Ridge Regression** machine learning model. The application is built with **Flask** and provides a simple web interface where users can enter weather-related parameters and receive a predicted FWI value. --- ## 🚀 Features * Predicts the **Fire Weather Index (FWI)** * Flask-based web application * User-friendly HTML interface * Data preprocessing using **StandardScaler** * Machine Learning model trained using **Ridge Regression** * Model and scaler saved using Pickle --- ## 🛠️ Technologies Used * Python * Flask * Scikit-learn * NumPy * Pandas * HTML * CSS * Pickle --- ## 📂 Project Structure ``` Algerian-Forest-Fire-Prediction/ │ ├── app.py ├── requirements.txt ├── README.md ├── ridge.pkl ├── scaler.pkl ├── templates/ │ └── home.html ├── 2.0-EDA And FE Algerian Forest Fires.ipynb └── 3.0-Model Training.ipynb ``` --- ## 📊 Input Features The model uses the following features for prediction: * Temperature * Relative Humidity (RH) * Wind Speed (Ws) * Rain * FFMC * DMC * ISI * Classes * Region --- ## 🎯 Output * **Fire Weather Index (FWI)** --- ## ⚙️ Installation Clone the repository: ```bash git clone github.com ``` Move into the project folder: ```bash cd Algerian-Forest-Fire-Prediction ``` Install the required dependencies: ```bash pip install -r requirements.txt ``` --- ## ▶️ Run the Application Start the Flask server: ```bash python app.py ``` Open your browser and visit: ``` 127.0.0.1 ``` --- ## 🧠 Machine Learning Workflow 1. Data Collection 2. Exploratory Data Analysis (EDA) 3. Feature Engineering 4. Data Preprocessing 5. Feature Scaling 6. Ridge Regression Model Training 7. Model Serialization using Pickl …