I combine my background in Food Engineering with Data Science, Machine Learning, Customer Analytics, Generative AI and Bioinformatics to work on scientific and real-world data problems.
My interests include machine learning, explainable AI, customer analytics, bioinformatics, food safety, toxicology and data-driven decision-support systems.
A structured portfolio of Data Science and AI case studies covering:
- Customer Analytics
- RFM & CLTV
- Customer Churn Prediction
- Recommendation Systems
- Unsupervised Learning
- Feature Engineering
- A/B Testing & Statistics
- Machine Learning
- Generative AI Decision Support
Explainable machine learning for genotoxicity risk prioritization and safer-alternative screening of food-contact chemicals.
Focus: Machine Learning • Explainable AI • Toxicology • Chemical Data
Bioinformatics workflow using real NCBI DNA sequences, Biopython, pandas and feature engineering.
Focus: Bioinformatics • Biological Data • Python • Feature Engineering
Food composition and nutrient-density analysis using selected foods from TürKomp.
Focus: Food Data • Python • Pandas • Segmentation
Food-engineering data science project using the Open Food Facts API, feature engineering and nutrition-based segmentation.
Programming & Data
Python • SQL • Pandas • NumPy
Machine Learning
Scikit-learn • XGBoost • LightGBM • CatBoost
Customer Analytics
RFM • CLTV • BG/NBD • Gamma-Gamma • Churn Analysis
Recommendation Systems
TF-IDF • Cosine Similarity • Apriori • Association Rules
Statistics
Hypothesis Testing • A/B Testing • ANOVA
Generative AI
Prompt Engineering • OpenAI API • Gemini API • Cohere API
Bioinformatics
Biopython • NCBI Biological Data
Tools
Git • GitHub • PyCharm • Jupyter Notebook • Docker
- Customer Analytics & Customer Lifetime Value
- Machine Learning & Model Evaluation
- Feature Engineering
- GenAI Decision-Support Systems
- Explainable AI
- Bioinformatics & Scientific Data
- SQL & Data Analytics
I am interested in interdisciplinary work combining:
Food Engineering → Biological & Chemical Data → Data Science → Machine Learning → Explainable AI
My academic interests particularly focus on computational approaches for:
- Food safety
- Toxicology
- Microorganisms
- Biological data
- Food-related bioinformatics