Introduction to PCA
Principal Component Analysis helps us reduce the number of dimensions in our data while keeping the most important information.
What is PCA?
Principal Component Analysis (PCA) is like finding the best angle to look at a complex object so you can understand it better!
Feature 1
Feature 2
Feature 3
Feature 4
Feature 5
Feature 6
PCA helps us see the most important features by reducing dimensions!
Original Data Points
Each colored sphere represents a data point with 3 features (X, Y, Z).
Click and drag to rotate the view!
Principal Components
PCA finds the directions where data varies most.
PC1: Most variance
PC2: Second most variance
Projection Process
Original points (faded) are projected onto principal components.
Bright points show the projected positions.
Data in Reduced Dimensions
What We've Accomplished
We've reduced our 3D data to 2D while preserving the most important information!
The spread of the data along PC1 and PC2 represents the variance in our original data.