Everything we've done so far in Phase 6 is supervised learning. You give the model examples with correct answers. It learns to predict.

K-Means is different. There are no correct answers. You hand it raw data and say: find me the groups.

It does. And those groups are often surprisingly useful.

Customer segments. Document topics. Image compression. Anomaly detection. All of these use clustering. None of them have labels.

What You'll Learn Here

How K-Means finds groups step by step

What centroids and inertia are

The elbow method and silhouette score for picking K

Why initialization matters and what K-Means++ does

When K-Means fails and what the signs look like

Full working code with real datasets

How K-Means Works Step by Step

The algorithm is simple. You tell it K (the number of clusters you want). It does this:

Step 1: Pick K random points as starting centroids.

Step 2: Assign every data point to the nearest centroid.…