Whats the difference between KNN and K-Means

Great question! Though KNN (K-Nearest Neighbors) and K-Means sound similar, they serve very different purposes in machine learning:

KNN (K-Nearest Neighbors)

Feature Description

Type Supervised Learning Algorithm

Use Case Classification or Regression

How it works Given a data point, it looks at the 'k' closest labeled points and predicts the label based on majority vote (classification) or average (regression).

Training No real training — it just stores the training data.

Input A labeled dataset

Example Predict if an email is spam by looking at the 5 most similar emails in the dataset.

K-Means

Feature Description

Type Unsupervised Learning Algorithm

Use Case Clustering (grouping similar data)

How it works Divides the dataset into K clusters by minimizing the distance between data points and the center of their assigned cluster.

Training Learns by updating cluster centers iteratively.

Input An unlabeled dataset

Example Segmenting customers into groups based on purchasing behavior.

Feature KNN K-Means

Learning Type Supervised Unsupervised

Goal Predict label Group similar data (cluster)

Needs Labels Yes No

Uses “K” as Number of neighbors Number of clusters