Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is.

In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained.

0:00 Awesome song and introduction

0:30 Conceptual motivation for PCA

3:23 PCA worked out for 2-Dimensional data

5:03 Finding PC1

12:08 Singular vector/value, Eigenvector/value and loading scores defined

12:56 Finding PC2

14:14 Drawing the PCA graph

15:03 Calculating percent variation for each PC and scree plot

16:30 PCA worked out for 3-Dimensional data

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75.25 GEEK