Data Science and Machine Learning Guides

A collection of practical, hands-on guides for data science, machine learning, and Python development. Each guide includes working code, real examples, and business-focused explanations.

📚 Guides

Machine Learning

• Handling Missing Data - Compare 7 different strategies with performance benchmarks

🎯 Who This Is For

• Data scientists preparing presentations for stakeholders
• ML engineers comparing different approaches
• Students learning practical data science
• Anyone who wants working code, not just theory

🚀 How to Use

1. Clone the repository
2. Open any .ipynb file in Jupyter
3. Run the cells and experiment!

🤝 Contributing

Found an error? Have a suggestion? Open an issue or submit a PR!

📝 License

MIT License - feel free to use these guides in your own work