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🔬 Principal Component Analysis (PCA)
Dimensionality reduction & feature extraction · variance contribution · loading matrix · score plot
PCA transforms many correlated variables into a few uncorrelated principal components for dimension reduction, feature extraction and visualization. Each row is one sample; each column is one variable.
Input data (one sample per row; space / comma separated)
Variable names (comma-separated)
📊 Overview
📈 Variance Explained (Scree Plot)
📋 Variance Table
📋 Loading Matrix (variables × PCs)
📊 Visualization
PC1 vs PC2 Score Scatter
Variable Loading Plot (PC1 vs PC2)
About Principal Component Analysis (PCA)
PCA online: extract principal components from correlation or covariance matrices, with eigenvalues, variance contributions, scree plot, loadings and scores.
Learn more about this tool →
Cluster Analysis & EFA
Linear Regression
Discriminant Analysis
Data Transformation Tool
Correspondence Analysis
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