ºÝºÝߣ

PCA (Principal Component Analysis) is a technique used to simplify complex data sets by reducing their dimensionality. It transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides background on concepts like variance, covariance, and eigenvalues that are important to understanding PCA. It also includes an example of using PCA to analyze student data and identify the most important parameters to describe students.Read less