MelihGulum/Comprehensive-Data-Science-AI-Project-Portfolio

A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.

46
/ 100
Emerging

Spans 7 domains (ML, DL, audio, computer vision, NLP, data engineering, analytics) with 30+ projects using scikit-learn, TensorFlow, Kafka, Docker, and AWS/GCP. Each project includes end-to-end reproducible workflows with hyperparameter tuning (Optuna), model interpretability (SHAP), and real-time deployment via Flask. Covers specialized pipelines: Kafka streaming with PostgreSQL, web scraping ETL, transfer learning on medical imaging, and time-series forecasting with statistical validation.

270 stars.

No License No Package No Dependents
Maintenance 10 / 25
Adoption 10 / 25
Maturity 8 / 25
Community 18 / 25

How are scores calculated?

Stars

270

Forks

40

Language

Jupyter Notebook

License

Last pushed

Feb 28, 2026

Commits (30d)

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