Principal Component Analysis Explained Visually

http://setosa.io/ev/principal-component-analysis/

“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.”

PCA is very useful for pulling information out of large datasets, particularly multi-dimensional datasets, where correlations and relationships between different variables might otherwise have been difficult to see.

Principal Component Analysis Explained Visually