An intelligent shopping list based on the application of partitioning and machine learning algorithms


A grocery list is an integral part of the shopping experience of many consumers. Several mobile retail studies of grocery apps indicate that potential customers place the highest priority on features that help them to create and manage personalized shopping lists. First, we propose a new machine learning model written in Python 3 that predicts which grocery products the consumer will buy again or will try to buy for the first time, and in which store(s) the purchase will be made. Second, we introduce a smart shopping template to provide consumers with a personalized weekly shopping list based on their shopping history and known preferences. As the explanatory variables, we used available grocery shopping history, weekly product promotion information for a given region, as well as the product price statistics.

Keywords:Machine LearningPredictionLong short-term memoryConvolutional Neural NetworkGradient Tree BoostingPythonSklearnTensorflow