Principal Component Analysis (PCA) Explained Visually with Zero Math

Principal component analysis (PCA) is a technique that transforms high-dimensions data into lower-dimensions while retaining as much information as possible. PCA is extremely useful when working with data sets that have a lot of features. Common applications such as image processing, genome research always have to deal with thousands-, if not tens of thousands of columns. While having more data is always great, sometimes they have so much information in them, we would have impossibly long model training time and the curse of dimensionality starts to become a problem. I like to compare PCA with writing a book summary. Finding the time to read a 1000-pages book is a luxury that few can afford.