Select Features for Loan Default

Business Context

Northstar Lending is building a binary classification model to predict whether a personal loan applicant will default within 12 months. The risk team wants a feature selection approach that improves generalization, reduces overfitting, and keeps the final model interpretable for compliance review.

Dataset

You are given a historical underwriting dataset with applicant, credit, and behavioral variables collected at application time.

• Size: 120K loan applications, 60 candidate features
• Target: default_12m — defaulted within 12 months (1) vs not defaulted (0)
• Class balance: 18% positive, 82% negative
• Missing data: 12% missing in employment and income-related fields; 3% missing in bureau variables

Success Criteria

A good solution should:

• Achieve ROC-AUC >= 0.78 on the held-out test set
• Reduce the feature set from 60 candidates to a smaller, defensible subset without materially hurting performance
• Produce a feature selection process that is reproducible and explainable to risk and compliance stakeholders

Constraints

• The final model must remain interpretable enough for model risk management review
• Training runs should finish within 30 minutes on a standard CPU machine
• Feature selection must avoid leakage and be performed only using training folds

Deliverables

1. Build a baseline classification pipeline using all candidate features.
2. Implement at least two feature selection techniques and compare them.
3. Select a final feature set and justify why it is appropriate for this dataset.
4. Report evaluation metrics on validation and test data.
5. Summarize tradeoffs between predictive power, stability, and interpretability.