/housing-prediction-microservice

This repository houses a microservice designed for predicting housing prices based on machine learning models. It provides a REST API endpoint to interact with the prediction service, making it easy to integrate into various applications.

Primary LanguagePythonMIT LicenseMIT

Housing Prediction Microservice

Contributors Forks Stargazers Issues MIT License

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
  1. Install dependencies:
pip install -r requirements.txt
  1. 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 😊