Question: Explain the K-means clustering algorithm and its use cases.

Answer: K-means is an unsupervised clustering algorithm that partitions data into k clusters based on similarity. It aims to minimize the sum of squared distances between data points and their assigned cluster centroids.

Example:

Segmenting customers based on purchasing behavior to identify marketing strategies for different groups.