AI Engineer & Data Scientist focused on end-to-end machine learning systems, applied AI, and portfolio-grade data products.
I build practical machine learning and AI projects with an emphasis on real-world validation, leakage-safe modeling, feature engineering, and clear project documentation.
My current focus is on building a strong AI/Data Science portfolio through end-to-end projects in:
- Fraud detection
- Customer churn prediction
- Forecasting
- Recommender systems
- Applied machine learning pipelines
- Deployment-oriented ML systems
I also have an academic background in Project and Construction Management, with PhD-level research on machine learning applications for construction risk assessment.
Time-aware fraud detection pipeline using advanced feature engineering, UID-style features, LightGBM, XGBoost, CatBoost, and ensemble learning.
Tech: Python, pandas, scikit-learn, XGBoost, LightGBM, CatBoost
Focus: Fraud detection, feature engineering, leakage-safe validation, imbalanced classification
Result: ROC-AUC = 0.9292
Repository: peyamikenanoglu/ieee-fraud-detection
End-to-end customer churn prediction project using the WSDM KKBox dataset, with realistic time-aware validation instead of an overly optimistic random split.
Tech: Python, pandas, scikit-learn, XGBoost
Focus: Churn prediction, temporal validation, customer behavior modeling
Repository: peyamikenanoglu/kkbox-churn-prediction
Machine learning project for box office revenue prediction and movie recommendation using structured movie metadata.
Tech: Python, pandas, scikit-learn, gradient boosting models
Focus: Regression, recommender systems, feature engineering, model evaluation
Repository: peyamikenanoglu/tmdb-box-office-ml-portfolio
Languages & Core Tools
Python SQL Git GitHub
Data Science & Machine Learning
pandas NumPy scikit-learn XGBoost LightGBM CatBoost
Modeling Areas
Classification Regression Forecasting Churn Prediction Fraud Detection Recommender Systems
Engineering & Deployment
FastAPI Docker Streamlit
Model Interpretation
SHAP Permutation Importance Feature Importance
- Strengthening my GitHub portfolio with business-oriented machine learning projects
- Improving deployment skills with FastAPI, Docker, and Streamlit
- Building clean, reproducible ML pipelines suitable for professional review
- Expanding SQL and data engineering skills for AI/ML roles
My goal is to build practical AI systems that go beyond notebooks: structured repositories, reproducible pipelines, readable documentation, and deployable machine learning products.