Use ML models to predict opponent action tendencies based on game state features. The neural network analyzes hand strength, pot odds, position, and stack depth.
This prediction system uses a simplified neural network that extracts 8 key features from the game state (hand strength, pot odds, position, stack depth, etc.) and outputs probability distributions for fold, call, and raise actions. In production research, this would be trained on historical hand data using TensorFlow.js or similar frameworks. The current implementation demonstrates the feature engineering and prediction pipeline for academic purposes.