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Amex Default Prediction
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 🥉
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Jane Street Market Prediction
Overview
Predicting profitable trading opportunities from anonymized financial market data. The competition required building models that could identify actionable signals in noisy, high-dimensional market features.
Approach
- Feature selection and denoising on anonymized market signals
- Gradient boosting and neural network ensembles
- Custom utility-based optimization aligned with competition metric
- Time-aware validation to avoid lookahead bias