Principal Component Analysis — 18 Questions Answered

One-stop place for your most of the questions regarding PCA

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Principal Component Analysis (also called PCA) is one of the most essential topics in the fields of data science and machine learning. It has so many uses so that it is a trending topic in search engines.

I’ve already published many articles about this topic. From theory to practical implementation, I’ve covered most of the parts of this topic.

Today, I’m going to answer the questions you might have about PCA. So, today’s article is in the Q&A format. This article will be the one-stop place for your most of the questions regarding PCA. This is also a great summary of the articles that I’ve already published on PCA.

So, I invite you to read this article from beginning to end! Without further delay, let’s begin the Q&A session in PCA.

Question 1

What is PCA?

PCA is a linear dimensionality reduction technique (algorithm) that transforms a set of correlated variables (p) into a smaller k (k<p) number of uncorrelated variables called principal components while retaining as much of the variance in the original dataset as possible — Source: 11 Dimensionality reduction techniques you should know in 2021 (my…