dmgamage / house_price_prediction

Boston House Price Prediction

End to End Machine Learning Project

Welcome to the End to End Machine Learning Project! This project serves as a comprehensive example of a machine learning workflow, specifically tailored for predicting Boston house prices using the Boston Housing Prediction dataset. Whether you are a data scientist, developer, or enthusiast, this project aims to provide insights into best practices and a holistic view of the machine learning process.

Overview

Machine learning projects are multifaceted and involve several key components. This section provides an overview of the project's goals, scope, and the technology stack used. Additionally, it outlines the main features and functionalities of the project.

Key Features

• Data Preparation: An in-depth exploration of the Boston Housing Prediction dataset, including data cleaning, feature engineering, and any preprocessing steps necessary for model training.

• Model Development: Walkthroughs and analyses of the machine learning model development process, including the choice of algorithms, hyperparameter tuning, and model evaluation specific to predicting Boston house prices.

• Docker Integration: Learn how to containerize the project using Docker, facilitating reproducibility and ease of deployment.

• GitHub Actions: Explore the use of GitHub Actions for continuous integration, automated testing, and ensuring code quality throughout the development lifecycle.

• Deployment: Understand how to deploy the trained model for predicting Boston house prices to a production environment, with scripts and configurations provided in the deployment directory.

Project Structure

This section provides an overview of the project's directory structure, specifically organized for Boston house price prediction. Users can navigate through different sections such as data, notebooks, source code, tests, Docker integration, GitHub Actions, and deployment to get a better understanding of the project organization.

Getting Started

For those new to the project, this section outlines the initial steps to get started, including cloning the repository, installing dependencies, and exploring provided Jupyter notebooks specifically tailored for analyzing the Boston Housing Prediction dataset and developing a model for predicting house prices.

Contributing

Information on how to contribute to the project is crucial for fostering collaboration. This section provides guidelines for contributors, including steps for raising issues, making enhancements, and submitting pull requests.

License

The project is licensed under the MIT License, allowing users to freely use, modify, and contribute to the codebase.

Acknowledgments

Optional section for giving credit to contributors, libraries, or resources that significantly contributed to the project.

Feel free to use, modify, and contribute to this project. Your feedback and contributions are highly appreciated!

Happy exploring and coding! 🚀