Customers in online shops return at least half of the placed orders. This huge amount of return shipments results in high costs e.g. from a logistic point of view. To predict the return rate based on customer data and order information, machine learning techniques can be applied which are able to learn a powerful model for return prediction based on historical order data. This article introduces a hands-on approach for successfully applying machine learning in real world processes and shows a case study to predict the return shipment probability in an e-commerce scenario.
