#CurrencyPairPrediction . Ensemble Learning Combine multiple models for better prediction accuracy: Stacking: Use multiple base models + a meta-model Bagging: Combine models trained on different subsets (e.g., Random Forest) Boosting: Sequential training (e.g., XGBoost, LightGBM) B. Regime-Switching Models Markets behave differently in different conditions: Hidden Markov Models (HMMs): Detect market regimes (e.g., trending vs mean-reverting) Bayesian switching models: Adapt model behavior dynamically C. Reinforcement Learning Let the model learn by trading: Q-Learning Deep Q Networks (DQN) Proximal Policy Optimization (PPO)