Practical Data Science using Python - K-Means Clustering Optimization

The video tutorial explains K-means clustering, emphasizing its nondeterministic nature due to random initial centroids. It highlights the importance of data standardization to handle outliers and scale differences. Two methods for optimizing the number of clusters, K, are discussed: the elbow method, which uses the sum of squared errors, and the silhouette method, which evaluates cluster quality using silhouette scores. The tutorial concludes with a practical example using Python for customer segmentation.

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