Data Science, Linear Algebra, Dimensionality Reduction, Math.
the end, PCA is just rotating the data around to project the data on top of principal components. Of course, we only subtracted the mean from each feature column, so the dimension hasn’t changed. Now, if you read how to calculate PCA, one of the common steps you see is you calculate the sample