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.
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Posted May 12, 2024
Enhanced sales prediction accuracy using Random Forest, enabling early detection of successful deals and risk mitigation.