Clustering Of Library’s Patron Behavior Using Machine Learning

Abstract

Libraries collect a lot of important transaction data, but they rarely use this information to improve how consumers interact with them. This work tries to bridge this gap by offering a novel use of machine learning to analyze and classify library patron behavior. The KMeans clustering technique was utilised to categorize Patron based on their age range, checkouts, and renewals. Dimensionality reduction methods like PCA and t-SNE were used to visually clarify the generated patterns. The clustering model performed quite well, as evidenced by its Calinski-Harabasz Index of 320.12, Davies Bouldin Index of 0.45, and Silhouette Score of 0.62. Beyond these metrics, the study’s novelty lies in its practical implications—offering libraries a data-driven framework to tailor services, improve user satisfaction, and optimize resource allocation. This study shows the transformative potential of machine learning in library science offering a data-driven framework for libraries to personalize services, optimize book recommendations, and enhance outreach efforts based on patron behavior. Limitation of this study lies on the data bias which may affect generalizability due to demographic differences across libraries.