In this post, I explain what PCA is, when, and why to use it, and how to implement it in Python using scikit-learn. Also, I explain how to get the feature importance after a PCA analysis. — 1. Introduction & Background Principal Components Analysis (PCA) is a well-known unsupervised dimensionality reduction technique that constructs relevant features/variables through linear (linear PCA) or non-linear (kernel PCA) combinations of the original variables (features). In this post, we will only focus on the famous and widely used linear PCA method.