Application Development for Customer Segmentation Using an Unsupervised Learning Algorithm

Abstract

Making wise selections is a requirement for any business to produce healthy revenue. Every customer in a firm is unique in comparison to other possible customers, and each one has various preferences for and objections to the product. The clients are divided into groups based on shared traits including gender, age, interests, and spending patterns. Targeting a certain group of customers with specialized goods, services, and marketing methods is made simpler by the customer segmentation process. This study focuses on segmenting customers based on their income and spending scores. The segmentation is done using the KMeans clustering algorithm, an unsupervised learning mechanism, and the total data is divided into three clusters. The model is then implemented using Python and the Flask web framework.