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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 finds a new coordinate system in which every point has a new (x,y) value. The axes don t actually mean anything physical; they re combinations of height and weight called principal components that are chosen to give one axes lots of variation.