Principal Component Analysis (PCA) is one of those classical methods that have seen a renaissance that came with the machine learning approach to computing. In today’s world of AI, it is one of the key methods for reducing the dimensionality of multivariable datasets.
Unfortunately, to understand PCA, you should have some prior knowledge of linear algebra. Concepts such as the eigenvalues and eigenvectors are intertwined with PCA. If you are struggling with these concepts, this is where this tutorial comes to your rescue.
Without any prior knowledge of linear algebra and with a minimum use of formulae, but relying on Excel, this tutorial will teach you the basics of the PCA method. The spreadsheets with all the calculations are placed here, and the paper containing the tutorial in PDF format can be downloaded or viewed from here.