Abstract: A customer order prediction system can include a computing device configured to obtain customer order data characterizing a customer’s interaction with an electronic marketplace and to obtain environmental data characterizing at least one event expected to effect the customer’s ordering behavior. The computing device can be further configured to generate feature data based on the customer data and the environmental data wherein the feature data organizes the customer data and the environmental data based on characteristics of an order size estimation model. The computing device is also configured to determine a predicted customer order size using the order size estimation model and to adjust a delivery availability of the electronic marketplace based on the predicted order size.

Abstract: An approach generates a speed profile for one or more delivery locations. The speed profile corresponds to location data of a delivery order for a respective delivery location, and indicates a plurality of events associated with a movement of a delivery order to the respective delivery location. The approach generates, based on the speed profile and location data corresponding to the delivery order, feature data of the respective delivery location. The approach applies a machine learning model to the generated feature data to output a door-step time prediction for the respective delivery location. The door-step time prediction is based on a time difference between timestamps of two events of the plurality of events associated with the movement of the delivery order. The approach generates a planned delivery route for the one or more delivery locations, based on the one or more delivery locations and respective the door-step time predictions.