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

Is this true ? Some free courses: 1. Foundations of Data Science: K-Means Clustering in Python https://www.clcoding.com/2023/09/foundations-of-data-science-k-means.html 2. Python Data Structures https://www.clcoding.com/2024/01/python-data-structures.html | Python Coding | Facebook