Developed a credit risk framework for GE’s lending team using CRISP-DM and k-NN modeling. Demonstrated how predictive analytics can reduce default losses and improve risk management through scalable, automated workflows.
Predicts U.S. home loan approvals using 4.25M real HMDA 2023 applications — XGBoost, Random Forest, Logistic Regression, ROC-AUC 0.9932
HMDA mortgage lending disparity analyzer — denial rates, racial disparities, lending deserts, and lender benchmarking
CRA exam ratings scraper and analyzer — search FFIEC database, parse Performance Evaluations, identify CDFI partnership opportunities
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