Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques

Timofei Bogomolov, Malgorzata W. Korolkiewicz, Svetlana Bogomolova

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel
Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Original languageEnglish
Title of host publicationHandbook of Research on Big Data Clustering and Machine Learning
EditorsFausto Pedro Garcia Marquez
Place of PublicationUnited States of America
PublisherIGI Global
Number of pages31
ISBN (Electronic)9781799801078
ISBN (Print)9781799801061, 9781799815655
Publication statusPublished - 2020
Externally publishedYes


  • consumer food choices
  • purchasing patterns
  • fresh fruit and vegetables
  • public health implications


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