Principal Component Analysis (PCA) Visualizer

Explore how PCA works by visualizing principal components on your data. Simply input your bivariate data and see the magic unfold!

Enter your data for Variable 1 and Variable 2 as comma-separated values. Ensure that both variables have the same number of data points.

Variable 1 Data:

Enter comma-separated numerical values for the first variable.

Variable 2 Data:

Enter comma-separated numerical values for the second variable.

About PCA Visualizer

Principal Component Analysis (PCA) is a powerful technique used to reduce the dimensionality of data while retaining the most important information. In simpler terms, for bivariate data (data with two variables), PCA helps to find the directions (principal components) along which the data varies the most. This visualizer helps you understand these principal components by plotting your data points and overlaying the principal components as vectors. Enter your data, and see how PCA identifies the main axes of variation in your dataset! This tool is excellent for educational purposes and quick data insights.