Python · SQL · R · Machine Learning · Analytics Engineering · Data Visualization
Anthropic Claude API and MCP courses · Google Data Analytics coursework
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Three Value-at-Risk methodologies on an 8-asset portfolio, stress-tested through the COVID crash and the 2022 rate shock — ending in a concrete rebalancing recommendation.
A 5-layer dbt + DuckDB warehouse turning raw seeds into a business-facing metrics layer, with automated testing baked into every model and a published lineage DAG.
A spaCy → TF-IDF → XGBoost pipeline evaluated on WELFake articles, with notebooks and Streamlit app code for exploring single-article predictions.
Five model/resampling combinations benchmarked on a 0.17%-fraud dataset by PR-AUC, then a business cost function that turns a decision threshold into dollars per day.
A simulated GDPR/CCPA audit on 50k records, deliberately seeded with violations so every detector, scanner, and dbt masking model can be proven to catch them.
A monitoring experiment with replayed data and an injected shift — PSI + KS tests, an Airflow loop, and a Streamlit dashboard for reviewing drift signals.
A full-stack finance app on Plaid Production: a TypeScript cleaning pipeline, a 7-detector alerts engine, and an explainable 0–100 financial health score, synced daily.