📧 Email/SMS Spam Classifier with MLOps

Welcome to the Email/SMS Spam Classifier repository! This project demonstrates a machine learning model designed to classify emails or SMS messages as either spam or not spam. It incorporates MLOps principles, Docker for containerization, GitHub Actions for CI/CD, and deployment on Render.

📋 Contents

• Introduction
• Topics Covered
• Getting Started
• Live Demo
• Docker and CI/CD
• MLOps Integration
• Deploy on Render
• 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 showcases an Email/SMS Spam Classification system using machine learning. The project integrates MLOps best practices with Docker for consistent environment management, GitHub Actions for CI/CD, and deployment on Render for live usage.

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🔍 Topics Covered

• Machine Learning Models: Training models to classify emails and SMS as spam or not spam.
• Natural Language Processing (NLP): Techniques for processing and analyzing textual data.
• Model Evaluation: Assessing the performance of the classification model.
• MLOps: Implementing continuous integration and deployment pipelines for ML projects.
• Docker: Containerizing the application for seamless deployment.
• CI/CD: Automating tests, builds, and deployments with GitHub Actions.
• Render: Deploying the application for live usage.

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🚀 Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-SMS-Spam-Classifier-with-MLOps.git

2. Navigate to the project directory:

   cd ML-Project-Email-SMS-Spam-Classifier-with-MLOps

3. Create a virtual environment and activate it:

   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:

   python app.py

6. Open your browser and visit:

   http://127.0.0.1:5000/
   

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🎉 Live Demo

Check out the live version of the Email/SMS Spam Classifier app here.

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🐳 Docker and CI/CD

Docker

This project is containerized using Docker to ensure that the environment is consistent across different systems.

1. Build the Docker image:

   docker build -t spam-classifier .

2. Run the Docker container:

   docker run -p 5000:5000 spam-classifier

3. Visit the application:

   http://127.0.0.1:5000/
   

CI/CD with GitHub Actions

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.

You can find the CI/CD workflow file in .github/workflows/ci-cd.yml.

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🛠️ MLOps Integration

This project integrates MLOps principles to manage the machine learning lifecycle efficiently:

1. Model Versioning: Keep track of different versions of the model using version control.
2. Automated Pipelines: Automate training, testing, and deployment pipelines using CI/CD.
3. Monitoring: Implement monitoring tools to track model performance in production.

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🌐 Deploy on Render

To deploy this application on Render, follow these steps:

1. Sign up for Render: Visit Render and sign up for an account.

2. 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.

3. Deploy: Render will automatically build and deploy your application. Once the deployment is successful, your application will be live.

4. Access your live app: Your application will be accessible via a Render-generated URL.

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🌟 Best Practices

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.

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❓ FAQ

Q: What is the purpose of this project? A: This project classifies emails and SMS as spam or not spam, demonstrating the use of machine learning, MLOps practices, Docker, and CI/CD pipelines.

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.

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🛠️ Troubleshooting

Common issues and solutions:

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

• Issue: CI/CD Pipeline Failing Solution: Check the GitHub Actions logs for errors and ensure all tests pass locally before committing.

• Issue: Model Accuracy Low Solution: Verify that the training data is preprocessed correctly and consider tuning the hyperparameters of the model.

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🤝 Contributing

Contributions are welcome! Here's how you can contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/new-feature

3. Make your changes:

   • Add 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 these resources for more insights into MLOps, Docker, CI/CD, and machine learning:

• MLOps Guide: MLOps Community
• Docker Official Documentation: docs.docker.com
• GitHub Actions Documentation: docs.github.com
• Render Documentation: render.com/docs
• Machine Learning Tutorials: Kaggle

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💪 Challenges Faced

Some challenges during development:

• Setting up the MLOps pipeline to automate the lifecycle of the ML model.
• Configuring Docker for consistent environment deployment.
• Ensuring that the model generalizes well to new, unseen data.

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📚 Lessons Learned

Key takeaways from this project:

• Gained experience in implementing MLOps practices for machine learning projects.
• Learned the importance of containerization in ensuring environment consistency.
• Developed an understanding of CI/CD pipelines for deploying machine learning applications.

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🌟 Why I Created This Repository

This repository was created to demonstrate how to build, train, and deploy an email/SMS spam classification model while applying MLOps best practices for automation and continuous improvement.

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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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