Suppose we have a given dataset whose variables represent different things. For instance, one of them could represent the time a user spends on the phone while another one can represent the continent the user lives in (with a number, for example 1 for America and 2 for Europe, etc.).
Does it make sense to use PCA or normalize these datasets? I tend to see that people usually normalize their datasets before using them to train a machine learning model, or even use PCA to reduce the dimensionality of the problem. Would these approaches be correct in a dataset where the variables represent totally different things (i.e. they are not all samples of a signal during time or coordinates in the space, they are things that have very different natures)?