K-Means Clustering

Task

Apply K-Means clustering to the iris dataset and compare cluster labels with true labels.

Why It Matters

Clustering finds natural groupings in data without labels - useful for customer segmentation, anomaly detection, and exploratory analysis. K-Means is fast and intuitive, making it a go-to algorithm for clustering tasks.

Instructions

1. Examine the setup code and data
2. Modify the code to solve the puzzle
3. Click Run or press Ctrl+Enter to execute