We will then look at the two methods namely the Elbow and Silhouette methods, by which we can calculate the optimum number of clusters within a given dataset. Then, we will go through the working principle of the K-means algorithm, after which we shall implement and end to end code in which we shall implement this algorithm to perform customer segmentation using the ‘Mall_Customers.csv’ dataset. We will first have a brief overview of what is meant by clustering, followed by understanding what the K-means algorithm is. In this tutorial, we will learn how to apply the K-means clustering in Sklearn library. 5.5 Applying Kmeans with 5 Clusters (K=5).We’re committed to supporting and inspiring developers and engineers from all walks of life.
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