IRIS DATA SET ANALYSIS USING PRINCIPAL COMPONENT ANALYSIS

Abstract

When there are several parameters in a particular data set, the accuracy of its model reduces. It is known as the Curse of dimensionality. Principal Component Analysis is applied to increase the accuracy of a particular data model. In this paper, I took a set of data called Iris which already exists in RStudio. Iris data consists of 150 observations and 4 features. Libraries that I used are biplot and screenplot. Using the function princomp(), I proceeded to do the PCA. After its execution, I found the PCA object using the function summary(). Finally, I plotted the components of PCA to give a much better visual representation to look at the data.