ML Project - Git, GitHub & GitHub Actions Demo

A comprehensive demo repository showing Git basics, GitHub collaboration, and GitHub Actions CI/CD with a machine learning project.

Project Overview

This project implements a simple Logistic Regression classifier for the Iris dataset from scikit-learn.

Features

• Data loading and preprocessing
• Logistic Regression model training
• Model evaluation and persistence
• Automated testing with pytest
• Code quality checks with flake8
• CI/CD pipeline with GitHub Actions
• Docker containerization

Repository Structure

ml-app/ ├── .github/workflows/ # GitHub Actions workflows ├── src/ # Source code ├── tests/ # Test cases ├── models/ # Trained models ├── requirements.txt # Dependencies └── README.md

Git Basics

Common Git Commands

Clone repository git clone

Check status git status

Add files to staging git add .

Commit changes git commit -m "Descriptive commit message"

Push to remote git push origin main

Create and switch to new branch git checkout -b feature/new-feature

Merge branches git merge feature/new-feature

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GitHub Actions

This repository includes a CI/CD workflow that:

• Runs on push/pull requests to main branch
• Installs dependencies
• Runs flake8 linting
• Executes pytest tests
• Builds Docker image
• Uploads test results and Docker image as artifacts

Getting Started

1. Create virtual environment and install dependencies: python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -r requirements.txt

2. Train the model: python src/train.py

3. Run tests: pytest tests/

4. Run linter: flake8 src/ tests/

CI/CD Pipeline

The GitHub Actions workflow automatically:

• Checks code quality
• Runs all tests
• Builds Docker container
• Uploads artifacts

See .github/workflows/ci.yml for details.