top of page


Updated: Jul 14, 2020

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. PCA is mostly used as a tool in exploratory data analysis and for making predictive models

Click and try the web Application

Run the Shiny App!!!


bottom of page