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https://github.com/md-emon-hasan/ml-project-email-sms-spam-classifier-with-mlops
📧 ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
https://github.com/md-emon-hasan/ml-project-email-sms-spam-classifier-with-mlops
data-science eda email-classification machine-learning-projects nlp spam-classification text-classification
Last synced: 2 months ago
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📧 ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-email-sms-spam-classifier-with-mlops
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2023-08-07T09:59:05.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-09-08T07:25:06.000Z (4 months ago)
- Last Synced: 2024-09-09T07:32:26.483Z (4 months ago)
- Topics: data-science, eda, email-classification, machine-learning-projects, nlp, spam-classification, text-classification
- Language: Jupyter Notebook
- Homepage: https://ml-project-email-sms-spam-classifier.onrender.com
- Size: 3.49 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# 📧 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](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [Docker and CI/CD](#docker-and-ci-cd)
- [MLOps Integration](#mlops-integration)
- [Deploy on Render](#deploy-on-render)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 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.
---
## 🔍 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.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-SMS-Spam-Classifier-with-MLOps.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-Email-SMS-Spam-Classifier-with-MLOps
```
3. **Create a virtual environment and activate it:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application:**
```bash
python app.py
```
6. **Open your browser and visit:**
```
http://127.0.0.1:5000/
```
---
## 🎉 Live Demo
Check out the live version of the Email/SMS Spam Classifier app [here](https://ml-project-email-sms-spam-classifier.onrender.com).
---
## 🐳 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:**
```bash
docker build -t spam-classifier .
```
2. **Run the Docker container:**
```bash
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`.
---
## 🛠️ 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.
---
## 🌐 Deploy on Render
To deploy this application on Render, follow these steps:
1. **Sign up for Render:** Visit [Render](https://render.com) 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.
---
## 🌟 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.
---
## ❓ 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](#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.
---
## 🛠️ 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.
---
## 🤝 Contributing
Contributions are welcome! Here's how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Add features, fix bugs, or improve documentation.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 📚 Additional Resources
Explore these resources for more insights into MLOps, Docker, CI/CD, and machine learning:
- **MLOps Guide:** [MLOps Community](https://mlops.community/)
- **Docker Official Documentation:** [docs.docker.com](https://docs.docker.com/)
- **GitHub Actions Documentation:** [docs.github.com](https://docs.github.com/en/actions)
- **Render Documentation:** [render.com/docs](https://render.com/docs)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
---
## 💪 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.
---
## 📚 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.
---
## 🌟 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.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---
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