Predicted rider retention for a taxi service and identified most significant factors that contributed to it. Achieved an 80% accuracy with a catboost model, which was chosen for its interpretability.
Identified the most important factors contributing to user adoption for a product. Achieved a 96% accuracy with an SVM model, and found user length and opting into the mailing list as the most significant predictors.
Created prediction algorithm for determining if a customer will cancel at the moment of booking. After eliminating numerous data leakage sources, we achieved a 90% AUROC with a catboost classification model.