Principal Component Analysis explained visuallyBy Victor Powell with text by Lewis Lehe Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize. 2D example First, consider a dataset in only two dimensions, like (height, weight). This dataset can be plotted as points in a plane. But if we want to tease out variation, PCA
Explained Visually
