Learn the fundamentals of Principal Component Analysis (PCA) in this comprehensive 22-minute video tutorial. Explore this powerful data analysis and machine learning method step-by-step, understanding its ability to identify patterns in complex datasets and determine the most important variables. Discover how PCA uses Singular Value Decomposition to simplify and explain data relationships. Follow along as the tutorial covers 2D and 3D data examples, finding principal components, calculating loading scores, creating PCA graphs, and interpreting scree plots. Gain insights into practical applications of PCA in R and learn how to determine the optimal number of principal components for your analysis.
Overview
Syllabus
: Points 5 and 6 are not in the right location
Awesome song and introduction
Conceptual motivation for PCA
PCA worked out for 2-Dimensional data
Finding PC1
Singular vector/value, Eigenvector/value and loading scores defined
Finding PC2
Drawing the PCA graph
Calculating percent variation for each PC and scree plot
PCA worked out for 3-Dimensional data
: Points 5 and 6 are not in the correct location
Taught by
StatQuest with Josh Starmer
