Demonstrates a thorough method of client segmentation using K-Means clustering on a dataset of mall patrons, utilizing behavioral and demographic characteristics like age, yearly income, and spending score. To find underlying patterns and relationships, the technique starts with data exploration and visualization. A noticeable inflection point in the inertia plot indicates that the Elbow Method is used to estimate the ideal number of clusters, which directs the choice of a suitable cluster count for significant segmentation. Customers are then divided into discrete segments using the K-Means technique, and characteristic profiles are revealed by interpreting each cluster centroid. Additionally, the solution is implemented as a web application built with Flask, which allows users to interactively examine their segmentation findings and administrators to control user activities. The results show that by providing data-driven, customized marketing and consumer engagement tactics, unsupervised machine learning approaches combined with intuitive online interfaces can greatly enhance decision-making in retail settings. Keywords: customer Segmentation, K-Means, Clustering.
B. et al. (Sat,) studied this question.