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K-Means Clustering Algorithm: A Detailed Guide

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K-Means Clustering Algorithm: A Detailed Guide

Learn how to implement the K-means clustering algorithm using scikit-learn. Explore step-by-step examples, feature scaling, and effective methods for handling outliers.

K-Means is a popular clustering algorithm used in data analysis and machine learning to group data points into a specified number of clusters, k, based on their similarity. It works by assigning each data point to the cluster whose center (called a centroid) is closest to it, then recalculating the centroids until the assignments stop changing or the improvement becomes minimal. The main goal of K-Means is to minimize the Within-Cluster Sum of Squares (WCSS)—a measure of how tightly the points i

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