Date of Graduation
5-2025
Document Type
Thesis
Degree Name
Bachelor of Science in Data Science
Degree Level
Undergraduate
Department
Data Science
Advisor/Mentor
Schubert, Karl
Committee Member
Juang, Michael
Second Committee Member
Sharma, Nikhil
Abstract
Retail success is influenced by a store's demographic and environmental context, both of which impact item-level sales performance. This study applies machine learning techniques to optimize item allocation based on club attributes at Sam’s Club locations. By analyzing store- specific factors such as proximity to universities, income levels, and regional preferences, the research identifies patterns that contribute to product demand. The results offer insights into how clubs can enhance inventory decisions, improving sales outcomes while reducing inefficiencies. This study reinforces the value of data-driven retail strategies and presents a practical framework for implementing predictive models in a real-world business context.
Keywords
machine-learning; assortment; retail; demographics; attributes
Citation
Negron, H. (2025). Attribute Based Assortment Using Machine-Learning. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/22
