Utilized Random Forest algorithm to improve sales opportunity prediction accuracy from 0.61 to 0.85, enabling early detection of winning or at-risk deals three months in advance, leading to a 24% increase in successful deal predictions.

Implemented a hybrid modeling approach including Hard Voting, Soft Voting, Stacking, Base Model Classifier, and Dynamic Model Selection techniques to optimize model performance for predicting deal outcomes.