Welcome to the Boston House Price Prediction System repository! This project leverages machine learning to predict housing prices based on various features. It integrates Docker for containerization, GitHub Actions for CI/CD, and deployment on Render for live hosting.
- Introduction
- Topics Covered
- Getting Started
- Live Demo
- Docker and CI/CD
- Deploy on Render
- Best Practices
- FAQ
- Troubleshooting
- Contributing
- Additional Resources
- Challenges Faced
- Lessons Learned
- Why I Created This Repository
- License
- Contact
This repository showcases a machine learning project aimed at predicting housing prices in Boston based on various features. The project utilizes Docker for containerization, GitHub Actions for CI/CD, and is deployed on Render for a live demonstration.
- Machine Learning Models: Training models to predict housing prices.
- Data Preprocessing: Techniques for cleaning and preparing the Boston housing dataset.
- Model Evaluation: Assessing the performance of the regression model.
- Deployment: Implementing the model as a web service using Flask.
- Docker: Containerizing the application for consistent deployment across environments.
- CI/CD: Automating tests and deployments with GitHub Actions.
- Render: Deploying the application on Render for live access.
To get started with this project, follow these steps:
-
Clone the repository:
git clone https://github.com/Md-Emon-Hasan/Ml-Project-Boston-House-Price-Prediction-using-Docker-and-GitHub-Action-with-Deployment.git
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Navigate to the project directory:
cd Ml-Project-Boston-House-Price-Prediction-using-Docker-and-GitHub-Action-with-Deployment -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
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Open your browser and visit:
http://127.0.0.1:5000/
Check out the live version of the Boston House Price Prediction app here.
This project is containerized using Docker to ensure that the environment is consistent across different systems.
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Build the Docker image:
docker build -t boston-house-price-predictor . -
Run the Docker container:
docker run -p 5000:5000 boston-house-price-predictor
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Visit the application:
http://127.0.0.1:5000/
This project uses GitHub Actions for continuous integration and deployment. Each commit triggers the following workflow:
- Linting and Testing: Automatically runs linting and tests to ensure code quality.
- Build and Deploy: Builds the Docker image and deploys the application to a cloud platform (e.g., Render, Heroku).
You can find the CI/CD workflow file in .github/workflows/ci-cd.yml.
To deploy this application on Render, follow these steps:
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Sign up for Render: Visit Render and sign up for an account.
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Create a new Web Service:
- Select "New Web Service" from your Render dashboard.
- Connect your GitHub repository.
- Select your desired branch (e.g.,
main) and set up the build and runtime settings.
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Deploy: Render will automatically build and deploy your application. Once the deployment is successful, your application will be live.
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Access your live app: Your application will be accessible via a Render-generated URL.
Recommendations for maintaining and improving this project:
- Model Updating: Continuously retrain the model with new data to improve accuracy.
- Container Security: Ensure the Docker container is secure and free from vulnerabilities.
- Error Handling: Implement comprehensive error handling in both the app and the CI/CD pipeline.
- Documentation: Keep the documentation up-to-date with the latest changes and improvements.
Q: What is the purpose of this project? A: This project predicts housing prices in Boston based on various features, demonstrating the use of machine learning, Docker, and CI/CD practices.
Q: How can I contribute to this repository? A: Refer to the Contributing section for details on how to contribute.
Q: Can I deploy this app on cloud platforms? A: Yes, you can deploy the Dockerized app on platforms such as Heroku, Render, or AWS.
Common issues and solutions:
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Issue: Docker Container Not Running Solution: Ensure that Docker is properly installed and the image was built successfully.
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Issue: CI/CD Pipeline Failing Solution: Check the GitHub Actions logs for errors and ensure all tests pass locally before committing.
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Issue: Model Accuracy Low Solution: Verify that the training data is preprocessed correctly and consider tuning the hyperparameters of the model.
Contributions are welcome! Here's how you can contribute:
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Fork the repository.
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Create a new branch:
git checkout -b feature/new-feature
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Make your changes:
- Add features, fix bugs, or improve documentation.
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Commit your changes:
git commit -am 'Add a new feature or update' -
Push to the branch:
git push origin feature/new-feature
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Submit a pull request.
Explore these resources for more insights into Docker, CI/CD, and machine learning:
- Docker Official Documentation: docs.docker.com
- GitHub Actions Documentation: docs.github.com
- Render Documentation: render.com/docs
- Machine Learning Tutorials: Kaggle
Some challenges during development:
- Setting up Docker for seamless deployment across environments.
- Configuring the CI/CD pipeline to automate the testing and deployment process.
- Ensuring the model performs well with a limited dataset.
Key takeaways from this project:
- Hands-on experience with Docker for containerizing machine learning applications.
- Setting up CI/CD pipelines for automated testing and deployment.
- Importance of continuous model improvement and deployment best practices.
This repository was created to demonstrate the end-to-end process of developing, containerizing, and deploying a machine learning model for predicting housing prices, with a focus on using Docker and CI/CD best practices.
This repository is licensed under the MIT License. See the LICENSE file for more details.
- Email: iconicemon01@gmail.com
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
Feel free to adjust and expand this template based on the specifics of your project and requirements.