I design machine learning models, interactive dashboards, and automated pipelines that turn complex data into smart,
scalable solutions—from predicting customer behavior to uncovering deep market insights.
MSc Business IntelligenceAnalytics & AutomationPower BI · Python · SQL · ML
From churn prediction and explainable ML to streamlined BI reporting, I connect data, tools, and teams so decisions are
clear, defensible, and quick to act on.
CurrentMSc in Business Intelligence, Aarhus University
Streamlit + PyCaret AutoML with SHAP explainability and OpenAI recommendations. Upload customer data, train models,
inspect churn drivers, and trigger retention strategies—all in one workflow.
A responsive personal portfolio designed to present academic background, skills, and data projects. Built with HTML/CSS
and powered by GitHub Pages + Formspree for direct contact.
Combines real-time parking API data, weather, and congestion to forecast availability. ML models (Random Forest,
XGBoost) feed interactive dashboards to cut search time and city congestion.
Built classification models on 2,000 phones with 21 features to predict price tiers. Highlights feature importance so
teams can shape market positioning and product strategy.
A CNN trained from scratch to classify candlestick chart images and predict next-day stock direction. Tuned learning
rates deliver stronger accuracy and faster convergence.