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.
This case study aims to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.
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