"""
PCA (Principal Component Analysis)
----------------------------------
PCA works by identifying the hyperplane that lies closet to the data and projecting
all dataset instances onto it.

Note that we should choose the hyperplane that retains the the most amount of variance
after the projection (because this'll be the situation where information is preserved most)

How choose the right hyperplane?
-> Choose the one that minimize the mean squared distance between the original dataset and its projection


"""
