📊 Principal Component Analysis (PCA)
Visualize eigenvectors and dimensionality reduction
Original Data with Principal Components
Transformed Data (PC Space)
PCA Results
Eigenvectors (Principal Components):
Click "Compute PCA" to see results
Understanding PCA
Principal Component Analysis finds the directions (eigenvectors) along which the data varies the most:
- Principal Component 1 (PC1): The direction of maximum variance
- Principal Component 2 (PC2): The direction of maximum variance orthogonal to PC1
- Eigenvalues: The variance along each principal component
- Variance Explained: How much of the total variance each PC captures
PCA transforms correlated variables into uncorrelated principal components, useful for dimensionality reduction, data visualization, and feature extraction.