In today’s highly competitive world, companies are constantly striving to stay ahead by implementing effective strategies. Central to this endeavor is the ability to make informed decisions, and leveraging data. However, through the application of machine learning techniques, such as K-Means clustering, businesses can effectively analyze vast datasets and identify target groups. Customer segmentation, facilitated by K-Means clustering, involves grouping data points with similar attributes. This method proves invaluable in helping businesses tailor their strategies to specific customer segments, thereby maximizing efficiency and effectiveness. One of the key steps in employing K-Means clustering is determining the optimal number of clusters. This is where the “elbow method” comes into play, providing a visual aid in identifying the point of diminishing returns in terms of clustering performance.