PCA (2D) — Principal Component Analysis Visualizer

📊 Dataset

Preset Points n 80 Correlation ρ 0.85 Spread X (σx) 1.20 Spread Y (σy) 1.20

🎛 Display

📐 Results

PC1 explained variance

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PC2 explained variance

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Explained variance ratio (PC1:PC2)

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PC1 direction (θ)

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Covariance σ₁₂

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λ₁ / λ₂ eigenvalues

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ℹ️ About

PCA finds the directions of maximum variance in data. PC1 (blue arrow) captures the most variance, PC2 (red, orthogonal) captures the rest. Eigenvalues λ of the covariance matrix give the variance along each axis. Click on the canvas to add custom points.