PCA Explorer - Interactive Visualization

Principal Component Analysis finds the directions of maximum variance in data. This explorer generates 100 correlated Gaussian points and shows you the covariance ellipse, PC1 and PC2 axes scaled by their eigenvalues, and the variance explained by each component. Adjusting the correlation ρ shows how PCA adapts to the data structure - when ρ approaches ±1, one component captures almost all variance.

• Generate correlated 2D data with adjustable ρ and spread σ
• See covariance ellipse and principal component axes emerge
• Watch variance explained bars update as correlation changes
• Understand why PCA finds eigenvectors of the covariance matrix
• See the connection between SVD and PCA

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.