Trained and optimized ensemble ML models integrating KNN, CNN, Random Forest, Neural networks and Regression techniques on multimodal datasets, improving model robustness under class imbalance and overfitting while achieving 90%+ accuracy and F1-score.

Designed experiments, tuned hyperparameters, and compared models to deliver stable, production-ready ensembles for real-world decision making.