We won’t go into the explanation of the mathematical concept, which can be somewhat complex. PCA, as opposed to the other techniques, only works with quantitative variables. How Does PCA Work? A 5-Step GuideĮven though our focus is PCA, let’s keep in mind the following five main principal component techniques that aim to summarize and visualize multivariate data. This could make it easier to visualize and understand the data.Īfter this tutorial, you will have a better understanding of the principal component analysis and how to apply it to real-life scenarios using the famous corrr package in R. In our scenario, PCA would pick three characteristics such as monthly expense, purchase frequency, and product rating.

This is done by transforming the original data into a lower-dimensional space while collating highly correlated variables together. It is a statistical approach that can be used to analyze high-dimensional data and capture the most important information from it. “But, what is principal component analysis?” This is where principal component analysis (PCA for short) comes in. Human beings cannot easily visualize more than three dimensions, hence visualizing customer data with five characteristics (dimensions) is not straightforward. To better analyze and draw actionable conclusions, we need to understand the data set or, at the very least, visualize it. Introduction to Principal Component Analysis (PCA)Īs a data scientist in the retail industry, imagine that you are trying to understand what makes a customer happy from a dataset containing these five characteristics: monthly expense, age, gender, purchase frequency, and product rating.

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