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.
- 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
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.
- 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.
To get started with this project, follow these steps:
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Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-SMS-Spam-Classifier-with-MLOps.git
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Navigate to the project directory:
cd ML-Project-Email-SMS-Spam-Classifier-with-MLOps -
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 Email/SMS Spam Classifier 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 spam-classifier . -
Run the Docker container:
docker run -p 5000:5000 spam-classifier
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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.
You can find the CI/CD workflow file in .github/workflows/ci-cd.yml.
This project integrates MLOps principles to manage the machine learning lifecycle efficiently:
- Model Versioning: Keep track of different versions of the model using version control.
- Automated Pipelines: Automate training, testing, and deployment pipelines using CI/CD.
- Monitoring: Implement monitoring tools to track model performance in production.
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 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.
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 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
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.
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.
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.
This repository is licensed under the MIT License. See the LICENSE file for more details.
- Email: [email protected]
- 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.