Principal Component Analysis Explained Visually
PCA is a cool technique mathematically, but in my many years of building models, I've never seen it result in a more accurate model. I could see it potentially being useful in situations where you're forced to use a linear/logistic model since you're going to have to do a lot of feature preprocessing, but tree ensembles, NNs, etc. are all able to tease out pretty complicated relationships among features on their own. Considering that PCA also complicates things from a model interpretability point of view, it feels to me like a method whose time has largely passed.