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

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

Links

• GitHub Repository