🎓 Student Performance Prediction System

Welcome to the Student Performance Prediction System repository! This project uses machine learning to predict student performance based on various features. It leverages Docker for containerization, GitHub Actions for CI/CD access.

📋 Contents

• Introduction
• Topics Covered
• Getting Started
• Docker and CI/CD
• Best Practices
• FAQ
• Troubleshooting
• Contributing
• Additional Resources
• Challenges Faced
• Lessons Learned
• Why I Created This Repository
• License
• Contact

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📖 Introduction

This repository demonstrates a machine learning project focused on predicting student performance based on various features. The project showcases the use of Docker for containerization, CI/CD pipelines with GitHub Actions demonstrations.

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🔍 Topics Covered

• Machine Learning Models: Building models to predict student performance.
• Data Preprocessing: Techniques for preparing and cleaning the student performance dataset.
• Model Evaluation: Evaluating the performance of different regression models.
• Deployment: Serving the model using Flask and deploying it as a web service.
• Docker: Containerizing the application to ensure consistency across different environments.
• CI/CD: Automating testing and deployment using GitHub Actions.

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🚀 Getting Started

To start with this project, follow these instructions:

1. Clone the repository:

   git clone https://github.com/Md-Emon-Hasan/ML-Project-Student-Performance-Prediction-using-Docker-and-CI-CD-Pipeline.git

2. Navigate to the project directory:

   cd ML-Project-Student-Performance-Prediction-using-Docker-and-CI-CD-Pipeline

3. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

4. Install the dependencies:

   pip install -r requirements.txt

5. Run the application locally:

   python app.py

6. Visit the application in your browser:

   http://127.0.0.1:5000/
   

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🐳 Docker and CI/CD

Docker

To containerize the application, follow these steps:

1. Build the Docker image:

   docker build -t student-performance-predictor .

2. Run the Docker container:

   docker run -p 5000:5000 student-performance-predictor

3. Access the application:

   http://127.0.0.1:5000/
   

CI/CD with GitHub Actions

The project employs GitHub Actions for continuous integration and deployment. The workflow includes:

• Linting and Testing: Ensures code quality through automated linting and testing.
• Build and Deploy: Builds the Docker image and deploys the app to a cloud platform.

Check out the workflow file in .github/workflows/ci-cd.yml.

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🌟 Best Practices

To maintain and enhance this project, consider the following practices:

• Model Maintenance: Regularly update the model with new data to improve accuracy.
• Security: Ensure Docker images are secure and free of vulnerabilities.
• Error Handling: Implement robust error handling in the application and CI/CD pipeline.
• Documentation: Keep documentation updated with project changes and improvements.

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❓ FAQ

Q: What is the goal of this project? A: The project aims to predict student performance based on various input features, showcasing the integration of machine learning, Docker, and CI/CD practices.

Q: How can I contribute to this project? A: Refer to the Contributing section for contribution guidelines.

Q: Can this application be deployed on other cloud platforms? A: Yes, the Dockerized app can be deployed on other platforms like Heroku, AWS, or Azure.

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🛠️ Troubleshooting

Common issues and their solutions:

• Issue: Docker Container Not Running Solution: Ensure Docker is installed and the image was built successfully.

• Issue: CI/CD Pipeline Errors Solution: Review the GitHub Actions logs for error details and ensure all tests pass before committing.

• Issue: Poor Model Performance Solution: Check data preprocessing steps and consider tuning the model’s hyperparameters.

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🤝 Contributing

Contributions are encouraged! Here’s how to contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/new-feature

3. Implement your changes:

   • Add new features, fix bugs, or improve documentation.

4. Commit your changes:

   git commit -am 'Add a new feature or update'

5. Push to the branch:

   git push origin feature/new-feature

6. Submit a pull request.

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📚 Additional Resources

Explore the following resources for more information:

• Docker Official Documentation: docs.docker.com
• GitHub Actions Documentation: docs.github.com
• Machine Learning Tutorials: Kaggle

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💪 Challenges Faced

Key challenges encountered during development:

• Configuring Docker to work seamlessly across different environments.
• Setting up a reliable CI/CD pipeline for automated testing and deployment.
• Achieving high model performance with the available dataset.

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📚 Lessons Learned

Important lessons from this project:

• Experience in using Docker for containerizing machine learning applications.
• Insights into setting up effective CI/CD pipelines.
• Understanding the importance of continuous improvement and deployment best practices.

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🌟 Why I Created This Repository

This repository was created to showcase the end-to-end process of building, containerizing, and deploying a machine learning model for predicting student performance, with a focus on using Docker and CI/CD best practices.

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📝 License

This repository is licensed under the MIT License. See the LICENSE file for more details.

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📬 Contact

• Email: iconicemon01@gmail.com
• WhatsApp: +8801834363533
• GitHub: Md-Emon-Hasan
• LinkedIn: Md Emon Hasan
• Facebook: Md Emon Hasan

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