The document describes a market basket analysis project to build a classification model that predicts which products a user will purchase in their next order. It details features related to users, products, and user-product interactions that were used to train an XGBoost model. The model achieved improved F1, accuracy, precision, and recall scores compared to the baseline. Feature importance results and conclusions about additional improvements are also presented.