Interact with the data from two hotels in Portugal to see the affect of altering customer markets and lead time on cancellation rates.
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.
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.