Principal Component Analysis (PCA)

Summary

A technique that transforms many correlated features into fewer uncorrelated components, ranked by how much variance they explain — used for dimensionality reduction and visualization.

• 1Apply PCA to a high-dimensional dataset and plot the first 2 components.
• 2Learn to interpret an explained-variance plot to decide how many components to keep.
• 3Understand PCA components lose direct interpretability — a real trade-off to weigh.