Overview

Predicting credit card default probability for American Express customers using anonymized transaction and account features. A large-scale tabular competition with heavy feature engineering requirements.

Approach

• Extensive feature engineering over time-series transaction histories
• Gradient boosting models (LightGBM, XGBoost, CatBoost)
• Aggregation features: rolling statistics, lag features, trend indicators
• Careful handling of missing values and categorical encodings

Result

287/4874 🥉

Links

• GitHub Repository