My Work
E-Commerce customer segmentation
An end-to-end machine learning pipeline built in Python using a ~1 million row retail dataset. It combines RFM feature engineering and K-means clustering with an XGBoost model to identify high-value B2B/B2C customers. The results and SHAP-based model explainability are presented in an interactive Streamlit dashboard for non-technical stakeholders.
- Python
- Feature Engineering
- RFM Analysis
- Machine learning
- K-means
- clustering
- XGBoost
- SHAP
- Streamlit
- Git
- Customer Segmentation
- E-commerce Analytics
Power BI Dashboard (Executive Tracker, DAX)
An interactive dashboard built with Power BI to visualize and analyze data.
- Power BI
- DAX
About Me
I am a computer science student at the Budapest University of Technology and Economics (BME), specializing in data-driven systems. I have a strong interest in data science at the intersection of technology and business.
Before switching careers, I ran my own business as a rope access technician, which taught me clear communication with clients and stakeholders and taking ownership.
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Get in Touch
I’m currently open to new opportunities and collaborations. Whether you have a question, want to work together, or just want to say hi, feel free to reach out!
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