66. K-Means Clustering: Find Groups Without Labels

Short summary

K-Means is an unsupervised clustering algorithm that finds natural groups in raw, unlabeled data by minimizing intra-cluster distances. This tutorial walks through the algorithm's four steps, explains centroids and inertia, and teaches two methods for selecting optimal K: the elbow method and silhouette score. It includes runnable Python code with scikit-learn, showing how K-Means recovers true clusters and applies to customer segmentation, anomaly detection, and other use cases.

• •K-Means finds clusters in unlabeled data without ground truth labels
• •Elbow method and silhouette scores determine optimal cluster count
• •Complete Python implementation with real use cases and convergence examples

Generated with AI, which can make mistakes.