Developed an end-to-end machine learning pipeline that extracts, cleans, and feature-engineers over 5.7k real-money hand histories from PokerNow.club. Leveraging advanced feature engineering (bet ratios, decision times, board evaluations with Ace detection, and dynamic positional metrics) and a custom LSTM model with dynamic bucketing, the system predicts bluff versus value betting with a test AUC of 0.77. Hyperparameter tuning, cross-validation, and class balancing were key to optimizing performance.
