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# 📧 Email/SMS Spam Classifier with MLOps
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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.
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![1](https://github.com/user-attachments/assets/ede0cb19-016c-4e1c-a4b0-6f3e43544e61)
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## 📋 Contents
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- [Introduction](#introduction)
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- [Topics Covered](#topics-covered)
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- [Getting Started](#getting-started)
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- [Live Demo](#live-demo)
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- [Docker and CI/CD](#docker-and-ci-cd)
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- [MLOps Integration](#mlops-integration)
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- [Deploy on Render](#deploy-on-render)
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- [Best Practices](#best-practices)
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- [FAQ](#faq)
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- [Troubleshooting](#troubleshooting)
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- [Contributing](#contributing)
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- [Additional Resources](#additional-resources)
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- [Challenges Faced](#challenges-faced)
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- [Lessons Learned](#lessons-learned)
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- [Why I Created This Repository](#why-i-created-this-repository)
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- [License](#license)
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- [Contact](#contact)
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---
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## 📖 Introduction
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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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---
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## 🔍 Topics Covered
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- **Machine Learning Models:** Training models to classify emails and SMS as spam or not spam.
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- **Natural Language Processing (NLP):** Techniques for processing and analyzing textual data.
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- **Model Evaluation:** Assessing the performance of the classification model.
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- **MLOps:** Implementing continuous integration and deployment pipelines for ML projects.
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- **Docker:** Containerizing the application for seamless deployment.
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- **CI/CD:** Automating tests, builds, and deployments with GitHub Actions.
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- **Render:** Deploying the application for live usage.
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---
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## 🚀 Getting Started
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To get started with this project, follow these steps:
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1. **Clone the repository:**
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```bash
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git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-SMS-Spam-Classifier-with-MLOps.git
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```
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2. **Navigate to the project directory:**
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```bash
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cd ML-Project-Email-SMS-Spam-Classifier-with-MLOps
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```
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3. **Create a virtual environment and activate it:**
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows use `venv\Scripts\activate`
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```
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4. **Install the dependencies:**
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```bash
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pip install -r requirements.txt
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```
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5. **Run the application:**
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```bash
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python app.py
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```
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6. **Open your browser and visit:**
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```
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http://127.0.0.1:5000/
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```
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---
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## 🎉 Live Demo
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Check out the live version of the Email/SMS Spam Classifier app [here](https://ml-project-email-sms-spam-classifier.onrender.com).
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---
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## 🐳 Docker and CI/CD
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### Docker
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This project is containerized using Docker to ensure that the environment is consistent across different systems.
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1. **Build the Docker image:**
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```bash
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docker build -t spam-classifier .
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```
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2. **Run the Docker container:**
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```bash
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docker run -p 5000:5000 spam-classifier
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```
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3. **Visit the application:**
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```
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http://127.0.0.1:5000/
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```
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### CI/CD with GitHub Actions
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This project uses GitHub Actions for continuous integration and deployment. Each commit triggers the following workflow:
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- **Linting and Testing:** Automatically runs linting and tests to ensure code quality.
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- **Build and Deploy:** Builds the Docker image and deploys the application to a cloud platform.
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You can find the CI/CD workflow file in `.github/workflows/ci-cd.yml`.
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---
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## 🛠️ MLOps Integration
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This project integrates MLOps principles to manage the machine learning lifecycle efficiently:
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1. **Model Versioning:** Keep track of different versions of the model using version control.
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2. **Automated Pipelines:** Automate training, testing, and deployment pipelines using CI/CD.
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3. **Monitoring:** Implement monitoring tools to track model performance in production.
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---
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## 🌐 Deploy on Render
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To deploy this application on Render, follow these steps:
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1. **Sign up for Render:** Visit [Render](https://render.com) and sign up for an account.
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2. **Create a new Web Service:**
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- Select "New Web Service" from your Render dashboard.
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- Connect your GitHub repository.
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- Select your desired branch (e.g., `main`) and set up the build and runtime settings.
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3. **Deploy:** Render will automatically build and deploy your application. Once the deployment is successful, your application will be live.
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4. **Access your live app:** Your application will be accessible via a Render-generated URL.
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---
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## 🌟 Best Practices
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Recommendations for maintaining and improving this project:
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- **Model Updating:** Continuously retrain the model with new data to improve accuracy.
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- **Container Security:** Ensure the Docker container is secure and free from vulnerabilities.
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- **Error Handling:** Implement comprehensive error handling in both the app and the CI/CD pipeline.
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- **Documentation:** Keep the documentation up-to-date with the latest changes and improvements.
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---
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## ❓ FAQ
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**Q: What is the purpose of this project?**
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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.
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**Q: How can I contribute to this repository?**
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A: Refer to the [Contributing](#contributing) section for details on how to contribute.
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**Q: Can I deploy this app on cloud platforms?**
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A: Yes, you can deploy the Dockerized app on platforms such as Heroku, Render, or AWS.
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---
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## 🛠️ Troubleshooting
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Common issues and solutions:
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- **Issue: Docker Container Not Running**
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*Solution:* Ensure that Docker is properly installed and the image was built successfully.
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- **Issue: CI/CD Pipeline Failing**
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*Solution:* Check the GitHub Actions logs for errors and ensure all tests pass locally before committing.
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- **Issue: Model Accuracy Low**
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*Solution:* Verify that the training data is preprocessed correctly and consider tuning the hyperparameters of the model.
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---
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## 🤝 Contributing
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Contributions are welcome! Here's how you can contribute:
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1. **Fork the repository.**
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2. **Create a new branch:**
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```bash
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git checkout -b feature/new-feature
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```
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3. **Make your changes:**
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- Add features, fix bugs, or improve documentation.
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4. **Commit your changes:**
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```bash
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git commit -am 'Add a new feature or update'
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```
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5. **Push to the branch:**
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```bash
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git push origin feature/new-feature
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```
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6. **Submit a pull request.**
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---
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## 📚 Additional Resources
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Explore these resources for more insights into MLOps, Docker, CI/CD, and machine learning:
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- **MLOps Guide:** [MLOps Community](https://mlops.community/)
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- **Docker Official Documentation:** [docs.docker.com](https://docs.docker.com/)
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- **GitHub Actions Documentation:** [docs.github.com](https://docs.github.com/en/actions)
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- **Render Documentation:** [render.com/docs](https://render.com/docs)
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- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
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---
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## 💪 Challenges Faced
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Some challenges during development:
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- Setting up the MLOps pipeline to automate the lifecycle of the ML model.
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- Configuring Docker for consistent environment deployment.
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- Ensuring that the model generalizes well to new, unseen data.
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---
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## 📚 Lessons Learned
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Key takeaways from this project:
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- Gained experience in implementing MLOps practices for machine learning projects.
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- Learned the importance of containerization in ensuring environment consistency.
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- Developed an understanding of CI/CD pipelines for deploying machine learning applications.
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---
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## 🌟 Why I Created This Repository
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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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---
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## 📝 License
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This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
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---
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## 📬 Contact
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- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
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- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
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- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
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- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
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- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
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---
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Feel free to adjust and expand this template based on the specifics of your project and requirements.
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---

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