I work with real-world data to support pricing, reporting, and operational decisions. My background combines applied economics with hands-on data analysis using Python, SQL, Excel, and Power BI.
- MSc Applied Economics | BSc Economics (Business Administration track)
- Experience analyzing ERP, sales, and pricing data in a live business environment
- Focus on clean data, clear logic, and measurable outcomes
- Interested in junior data analyst, BI analyst, and operations analytics roles
Regression | Python, XGBoost
- Built an end-to-end regression pipeline (cleaning, feature engineering, geospatial clustering, modeling).
- Added KMeans geo-clusters (lat/long) to capture location effects.
- Achieved R² ≈ 0.65 and MAE ≈ £128K, outperforming baseline models.
Repository: London_House_Price_Prediction
Classification | Python, scikit-learn
- Engineered predictive features (title extraction, family size, cabin deck).
- Compared multiple classifiers and selected the best-performing model.
- Achieved 0.844 validation accuracy.
Repository: Titanic_Machine_Learning_from_Disaster
Image Classification | Python
- Trained and evaluated multiple classifiers.
- Best model achieved 97.2% validation accuracy.
Repository: Digit-Recognizer