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Principal Component Analysis

Explore interactively how PCA finds the axes of maximum variance. Use the 2D view to drag points directly and the 3D view to rotate the data cloud.

PCA Variation

Drag the blue line to rotate the direction. The right view shows the points projected onto that line.

Drag the blue line to rotate the direction.
Points projected into one-dimensional space.
Variance explained by this line91.1%

PCA in 2D

Drag points to watch the principal components rotate. Shift+click adds a point, Alt+click removes one.

Drag points or reshape the cloud with sliders.
PC1
PC2
Mean
PC1 variance83.3%
PC2 variance16.7%
Mean(0.16, 0.24)
Points projected into PC1 (x) and PC2 (y).
PC1 axis
PC2 axis

PCA in 3D

Rotate the view to see how the principal axes align with the direction of maximum variance.

Example data
Drag to rotate. The axes show PC1, PC2, and PC3.
PC1
PC2
PC3
Points projected onto PC1 (x) and PC2 (y).
PC1 axis
PC2 axis
PC1 variance60.1%
PC2 variance37.4%
PC3 variance2.5%

Real World Example

Explore PCA on a real dataset from the UCI Wine study. See how the variables compress into a few principal components.

Calculate PCA with real world data

Wine Data from: Aeberhard, S. & Forina, M. (1992). Wine [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.

PCA real world example

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