niru17/Algerian-Forest-Fire-Linear-Regression-Model

# Algerian Forest Fire Linear Regression Model This project implements an **end-to-end Machine Learning pipeline** to predict the **Fire Weather Index (FWI)** for the Algerian Forest Fire dataset. The project uses **Ridge Regression** for prediction and is deployed as a **Flask web application** with an HTML frontend. --- ## 📌 Project Overview - **Dataset**: Algerian Forest Fire Dataset (UCI Machine Learning Repository) - **Target Variable**: Fire Weather Index (FWI) – a continuous float value - **Features**: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Initial Spread Index (ISI) - Classes - Region - **ML Model**: Ridge Regression (trained & serialized using `pickle`) - **Deployment**: Flask backend with HTML frontend form --- ## ⚙️ Tech Stack - **Python** (Flask, Scikit-learn, Pandas, Numpy) - **Frontend**: HTML (Jinja templates) - **Modeling**: Ridge Regression with Standard Scaler - **Deployment**: Local Flask server --- ## 🚀 How to Run Locally ### 1. Clone the repository ```bash git clone github.com cd Algerian-Forest-Fire-Linear-Regression-Model ``` ### 2. Create virtual environment ```bash python3 -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows ``` ### 3. Install dependencies ```bash pip install -r requirements.txt ``` ### 4. Run the Flask app ```bash python3 application.py ``` The app will start on 👉 `127.0.0.1` --- ## 🖼️ Application Workflow 1. User opens the **web form** (`home.html`). 2. Inputs values for features (Temperature, RH, Ws, Rain, etc.). 3. Form submits data to Flask backend (`/prediction_data`). 4. Backend scales the inputs → applies trained **Ridge model** → returns prediction. 5. Prediction (FWI score) is displayed on the webpage. --- ## 📂 Project Structure ``` . ├── application.py # Flask app ├── models/ │ ├── …