Data Mining for Dynamic Customer Segmentation: Unraveling Insights

Abstract

Effective decision-making is essential for every firm to earn high income. These days, there is intense rivalry, and every company is advancing using a unique set of techniques. We ought to make an informed choice based on evidence. Since each client is unique, we have no idea what they enjoy or what they purchase. But by using a variety of algorithms on the dataset, one may use machine learning techniques to filter through the data and identify the target group. In the absence of this, identifying a group of individuals with like interests and personalities within a sizable dataset will be exceedingly challenging and no better methods exist. The use of K-Mean clustering for customer segmentation aids in grouping data with comparable characteristics, which benefits the firm the most. We will use the elbow approach to determine the number of clusters, and then we will visualize the results.