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https://github.com/2003harsh/house-price-prediction-using-machine-learning
This project features a web app that predicts house prices using a linear regression model. Users can input details like location, square footage, bathrooms, and bedrooms through an HTML form. I've added a CI/CD pipeline with GitHub Actions, unit testing with pytest, and automated Docker containerization to improve deployment and robustness.
https://github.com/2003harsh/house-price-prediction-using-machine-learning
ci-cd data-analysis docker-image flask linear-regression machine-learning matplotlib mlops-workflow requests scikit-learn
Last synced: about 1 month ago
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This project features a web app that predicts house prices using a linear regression model. Users can input details like location, square footage, bathrooms, and bedrooms through an HTML form. I've added a CI/CD pipeline with GitHub Actions, unit testing with pytest, and automated Docker containerization to improve deployment and robustness.
- Host: GitHub
- URL: https://github.com/2003harsh/house-price-prediction-using-machine-learning
- Owner: 2003HARSH
- License: mit
- Created: 2024-08-07T13:09:15.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T11:12:16.000Z (2 months ago)
- Last Synced: 2024-10-10T08:03:32.038Z (about 1 month ago)
- Topics: ci-cd, data-analysis, docker-image, flask, linear-regression, machine-learning, matplotlib, mlops-workflow, requests, scikit-learn
- Language: Jupyter Notebook
- Homepage: https://hub.docker.com/r/2003harsh/house_price_prediction
- Size: 51.8 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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:
```bash
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:
```bash
pip install -r requirements.txt
```
## Running the Application
### Start the Flask Server
Run the Flask server with the following command:
```bash
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:**
```sh
docker pull 2003harsh/house_price_prediction
```
2. **Run the Docker Container:**
```sh
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:**
```json
{
"location": "Sarjapur",
"total_sqft": 1500,
"bath": 2,
"bhk": 3
}
```
**Response:**
```json
{
"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](LICENSE) file for details.
## Contact
For any questions or issues, please contact [[email protected]](mailto:[email protected]).