Learn PCA by building it with your own hands. Draw or generate data, center and standardize it, inspect the covariance matrix, watch eigenvectors emerge, and then step into a rotatable 3D scene where PCA finds the best low-dimensional summary of a cloud in space.
Start by making a dataset that has some shape. PCA is only interesting when there is structure to explain.
Add points or load a preset to begin the tutorial.