I'm a data scientist and analyst passionate about turning complex data into actionable insights. I recently completed an intensive Data Science and Machine Learning program at 4Geeks Academy, where I built and deployed real-world machine learning solutions across healthcare, finance, and AI.
- Built 4 seasonal Random Forest models on nearly 2 million records
- Deployed a GPT-4o powered RAG chatbot via Streamlit
- 🔗 Live App | GitHub
- Engineered time-series features from 1M+ real Czech banking transactions
- 85% accuracy with Random Forest + SMOTE for class imbalance
- SQL analysis showing defaulters take out loans 3x larger on average
- 🔗 GitHub
- Built a simulated premium billing dataset and reconciled billed vs. paid amounts across 150 invoices
- Produced an aging report (1-30/31-60/60+ days past due) using SQL and an Excel workbook with PivotTables and XLOOKUP
- Identified top past-due agencies and highest-risk lines of business
- 🔗 GitHub
- Healthcare analytics project using Python, SQL, Excel, Tableau, and Linear Regression to identify cost drivers behind medical insurance charges
- SQL analysis found smokers averaged $32,050 in charges vs. $8,441 for non-smokers, with obese smokers over 50 the highest-cost group at ~$47,369
- Built an Excel billing reconciliation workbook (formulas, data validation, PivotTables) flagging 194 of 1,337 records as under/overpaid
- Linear Regression model achieved R² of 0.80 on test data, with training/test performance closely matched
- 🔗 GitHub
- Built a Convolutional Neural Network for cats vs dogs image classification
- 🔗 GitHub
