Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
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.