I have data that includes continuous and categorical features. The task is regression and I am looking to remove features that are high correlated with other features (multicollinearity). To do this, I have used
pd.get_dummies to one hot encode my categorical features, calculated the correlation matrix, and then removed one variable in each pair of highly correlated variables. Is this the correct way to do this?