Housing Prediction Microservice

This repository contains a microservice for predicting housing prices based on machine learning models.

Overview

This microservice provides a REST API endpoint to predict housing prices. It utilizes a machine learning model trained on historical housing data to make predictions.

Installation

1. To run this microservice locally, follow these steps:

git clone https://github.com/Ruban2205/housing-prediction-microservice.git
cd housing-prediction-microservice

2. Install dependencies:

pip install -r requirements.txt

3. Start the microservice

python app.py

The microservice will start running locally at http://localhost:5000

API Usage

Endpoint

• POST /predict

Request Body

The /predict endpoint expects a JSON object with the following format:

{
  "area": 1500,
  "rooms": 3,
  "age": 15
}

• area (integer): The area of the house in square feet.
• rooms (integer): The number of rooms in the house.
• age (integer): The age of the house in years.

Response

The endpoint responds with a JSON object containing the predicted price:

{
  "predicted_price": 250000
}

Contributing

Thank you for considering contributing to the Housing Prediction Microservice! Please follow these guidelines:

1. Reporting Bugs: Search existing issues before reporting bugs.
2. Enhancements: Suggest new features or improvements via GitHub issues.
3. Code Contributions: Fork the repo, create a branch, and submit a pull request.
4. Pull Requests: Ensure tests are added for new code. Get a sign-off before merging.

Deployment

For deployment, ensure to set the necessary environment variables and configure the microservice to run in a production environment. This typically involves using a production-grade WSGI server like Gunicorn and setting up appropriate logging.

License

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

Star ⭐ this repository for Future use 😊