House Price Prediction uisng Machine Learning

Overview

This project implements a house price prediction model using linear regression, predicting prices based on features like location, total square feet, number of bathrooms, and bedrooms (BHK), with a Flask backend and a simple HTML frontend for user interaction. Building on the initial version, I've integrated a CI/CD pipeline using GitHub Actions for automated testing and deployment, added unit test cases with pytest to ensure code quality, and automated Docker containerization for consistent deployments across environments. These enhancements significantly improve the application's robustness, maintainability, and scalability, making it production-ready.

Features

• Linear Regression Model: Utilizes linear regression to predict house prices.
• Flask Backend: Handles API requests and serves the prediction model.
• HTML Form: Provides a user interface for inputting house details and receiving predictions.
• Experiment Tracking: Integrated MLflow for tracking experiments, providing deeper insights into model performance.
• Hyperparameter Tuning: Optimized model performance using GridSearchCV and Hyperopt.
• Interactive Visualizations: Leveraged MLflow's web interface for visualizing and comparing experiment results.
• CI/CD Pipeline: Implemented using GitHub Actions to automate testing and deployment.
• Unit Tests: Developed test cases using pytest to ensure application reliability.
• Automated Docker Containerization: Dockerized the application for easy deployment.

Technologies

• Python: Core programming language.
• Flask: Web framework for handling HTTP requests.
• scikit-learn: Machine learning library used for model training.
• HTML/CSS/JavaScript: Frontend technologies for creating a user interface.
• MLflow: Tool for experiment tracking.
• GitHub Actions: CI/CD for automated testing and deployment.
• Docker: Containerization tool for packaging the application.

Installation

Prerequisites

Ensure you have Python and pip installed on your system. You will also need the following Python libraries:

• Flask
• scikit-learn
• pandas
• numpy
• matplotlib
• pickle
• mlflow

Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/2003HARSH/House-Price-Prediction-using-Machine-Learning
cd House-Price-Prediction-using-Machine-Learning

You can install the required libraries using pip:

pip install -r requirements.txt

Running the Application

Start the Flask Server

Run the Flask server with the following command:

python app.py

The server will start and listen on http://127.0.0.1:5000/ .

Using the HTML Form

After the form is open enter the required values to get the output.

Installation using Docker

1. Pull the Docker Image:

   docker pull 2003harsh/house_price_prediction
   

2. Run the Docker Container:

   docker run -p 5000:5000 2003harsh/house_price_prediction
   

3. Access the Application:

   Open your web browser and navigate to http://localhost:5000 to use the app.

Plots

API Endpoint

POST /predict

Description: Receives house details and returns the predicted price.

Request Body:

{
    "location": "Sarjapur",
    "total_sqft": 1500,
    "bath": 2,
    "bhk": 3
}

Response:

{
    "predicted_price": 550000.00
}

Project Structure

• app.py: Flask backend script.
• model.pkl: Serialized linear regression model.
• index.html: Frontend HTML form for user input.
• README.md: This file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or issues, please contact [email protected].