🧮The beauty of K-means clustering lies in its ability to reveal hidden structures within data, grouping similar data points into clusters.
There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster. The term 'K' is a number of clusters. The model is evaluated through the Sum of square distance within the cluster and through Silhouette Analysis.