Build an MLP Baseline for Credit-Default Risk at a Fintech
Overview
What this challenge is about.
Train an MLP with regularization on credit-default data, compare to XGBoost with SHAP, and earn a verifiable certificate.
The Brief
What you'll do, and what you'll demonstrate.
Match or beat XGBoost on AUC, calibration, and approval-rate-at-default-rate using a single regularized MLP.
Earning criteria — what you'll demonstrate
- Apply regularization (dropout, weight decay) on tabular MLPs
- Compare deep models against strong tree baselines fairly
- Evaluate calibration on a credit-risk model
- Communicate model behavior to a CRO audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning
Master · Ai Ml
Fit score: 1
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesMachine Learning Engineer
Replacing tree baselines with calibrated MLPs and writing the CRO memo is exactly the kind of first project a junior MLE owns at a fintech.
This challenge sharpens
- mlp
- regularization
- calibration
Data Scientist
Model comparison with calibration and SHAP-based explanations is a canonical credit-risk data-scientist deliverable.
This challenge sharpens
- calibration
- shap
- tabular-deep-learning
Applied AI Scientist
Translating deep-learning parity into a CRO sign-off package mirrors the applied-AI-scientist's bridging role.
This challenge sharpens
- mlp
- calibration
- shap