Linear Regression Model Deployment with Flask

This project demonstrates the deployment of a Ridge regression model using Flask. The application serves predictions via a web interface and an API endpoint, enabling users to input data and receive predictions in real-time.

Project Structure

• application.py: The main Flask application file that handles requests, loads the model and scaler, and renders the web pages.
• models/ridge.pkl: The pre-trained Ridge regression model saved as a pickle file.
• models/scaler.pkl: The standard scaler for feature scaling, also saved as a pickle file.
• templates/index.html: HTML file for the main user interface.

Prerequisites

• Python 3.x
• Flask
• Scikit-learn
• Pandas, Numpy
• Pickle (for loading model files)

Installation

1. Clone the repository:

   git clone https://github.com/harishbalajib/LinearRegression.git
   cd LinearRegression

2. Install the required packages:

   pip install -r requirements.txt

3. Ensure the models directory contains the ridge.pkl and scaler.pkl files for the model and scaler, respectively.

Running the Application

Start the Flask application with:

python application.py

This will start a local server. Open a browser and navigate to http://127.0.0.1:5000/ to access the web interface.

Usage

1. Web Interface: The main page allows you to input data for predictions. Enter the data in the form fields and submit to get predictions from the Ridge regression model.
2. API Endpoint: You can also send a POST request to the /PredictData endpoint with JSON data to receive predictions.

Contributing

Feel free to open issues or submit pull requests with improvements or new features.

License

This project is licensed under the MIT License.