House Price Predictor

This repository hosts a Flask-based web application designed to predict house prices using a RandomForestRegressor model trained on housing data. It includes a CI/CD pipeline utilizing GitHub Actions for automated testing and deployment across different environments.

View deployed model

https://house-price-predictor-online-d4845b3ba057.herokuapp.com/

Features

• Flask API: Handles prediction requests through RESTful endpoints.
• Machine Learning Model: Utilizes RandomForestRegressor for predicting house prices.
• CI/CD Pipeline: Automated workflows for continuous integration and deployment using GitHub Actions.
• Frontend Interface: Provides a user-friendly interface for data input and displays predictions.

Project Structure

house-price-predictor/
│
├── static/
│   └── css/
│       └── style.css
├── templates/
│   └── index.html
├── tests/
│   └── app_test.py
├── .github/
│   └── workflows/
│       └── ci-cd.yaml
├── app.py
├── requirements.txt
├── Dockerfile
├── Procfile
└── README.md

Getting Started

Prerequisites

• Python 3.8+
• pip
• Docker (optional for containerization)
• Heroku CLI (optional for Heroku deployment)

Installation

Clone the repository:

git clone https://github.com/yourusername/house-price-predictor.git
cd house-price-predictor

Install the required dependencies:

pip install -r requirements.txt

Run the application locally:

python app.py

Usage

Access the web application by navigating to http://localhost:5000 after starting the server. Enter the required details in the provided form and submit to receive the predicted house price.

Testing

Execute the automated tests:

pytest

Deployment

The project is configured for deployment on Heroku using Docker. Updates to the main, dev, and staging branches trigger corresponding automated deployments through GitHub Actions.

Contributing

Contributions are welcome. Please open an issue to discuss proposed changes or enhancements.

License

Distributed under the MIT License. See LICENSE for more information.