In simple words, PCA is a method of extracting important variables (in the form of components) from a large set of variables available in a data set. The purpose of this article is to provide a complete and simplified explanation of principal component analysis, especially to demonstrate how you can perform this analysis using R. Principal component analysis (PCA) is the best, widely used technique to perform these two tasks. retain the most important dimensions/variables.Remove redundant dimensions or variables, and.
The two ways of simplifying the description of large dimensional datasets are the following: However, the growth has also made the computation and visualization process more tedious in the recent era. Due to the rapid growth in data volume, it has become easy to generate large dimensional datasets with multiple variables. In today’s Big Data world, exploratory data analysis has become a stepping stone to discover underlying data patterns with the help of visualization.