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https://github.com/md-emon-hasan/ml-project-iris-classifier-with-github-action
🌸 ML Project integrated with GitHub Actions for CI/CD. It automates testing and deployment, ensuring a streamlined and efficient workflow.
https://github.com/md-emon-hasan/ml-project-iris-classifier-with-github-action
ci-cd cicd data-science github-action iris-classification iris-dataset mahcine-learning
Last synced: about 1 month ago
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🌸 ML Project integrated with GitHub Actions for CI/CD. It automates testing and deployment, ensuring a streamlined and efficient workflow.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-iris-classifier-with-github-action
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-08-27T06:44:18.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-28T11:48:12.000Z (4 months ago)
- Last Synced: 2024-08-28T12:57:57.472Z (4 months ago)
- Topics: ci-cd, cicd, data-science, github-action, iris-classification, iris-dataset, mahcine-learning
- Language: Python
- Homepage: https://iris-classifier-flask.onrender.com
- Size: 82.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🌸 Iris Flower Classification with CI/CD Integration
Welcome to the **Iris Flower Classification with CI/CD Integration** repository! This project leverages machine learning to classify Iris flowers into different species based on their features, with continuous integration and deployment (CI/CD) powered by GitHub Actions.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [CI/CD with GitHub Actions](#cicd-with-github-actions)
- [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 a machine learning project aimed at classifying Iris flowers into different species. The project incorporates continuous integration and deployment (CI/CD) using GitHub Actions for automating tests and deployment.
---
## 🔍 Topics Covered
- **Machine Learning Models:** Training models to classify Iris flower species.
- **Data Preprocessing:** Techniques for cleaning and preparing the Iris dataset.
- **Model Evaluation:** Assessing the performance of the classification model.
- **CI/CD:** Automating tests and deployments with GitHub Actions.
- **Deployment:** Implementing the model as a web service using Flask and deploying it on cloud platforms.
---
## 🚀 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-Iris-Classifier-with-GitHub-Action.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-Iris-Classifier-with-GitHub-Action
```
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 Iris Flower Classification app [here](https://iris-classifier-flask.onrender.com).
---
## 🤖 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 and deploys the application to a cloud platform.
You can find the CI/CD workflow file in `.github/workflows/ci-cd.yml`.
---
## 🌟 Best Practices
Recommendations for maintaining and improving this project:
- **Model Updating:** Continuously retrain the model with new data to improve accuracy.
- **CI/CD Enhancement:** Monitor and improve the CI/CD pipeline for faster and more reliable deployments.
- **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 Iris flowers into different species using machine learning and demonstrates the use of CI/CD for deployment.
**Q: How can I contribute to this repository?**
A: Refer to the [Contributing](#contributing) section for details on how to contribute.
---
## 🛠️ Troubleshooting
Common issues and solutions:
- **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 CI/CD and machine learning:
- **GitHub Actions Documentation:** [docs.github.com](https://docs.github.com/en/actions)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
---
## 💪 Challenges Faced
Some challenges during development:
- Setting up the CI/CD pipeline for automated testing and deployment.
- Ensuring the model performs well with a limited dataset.
---
## 📚 Lessons Learned
Key takeaways from this project:
- Hands-on experience with CI/CD pipelines for automating testing and deployment.
- Importance of continuous model improvement and deployment best practices.
---
## 🌟 Why I Created This Repository
This repository was created to demonstrate the end-to-end process of developing, integrating CI/CD, and deploying a machine learning model for classifying Iris flowers.
---
## 📝 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/)
---
Feel free to adjust and expand this template based on the specifics of your project and requirements.
---