I have a large dataset with 800 columns and 6,000,000 rows with many dummy variables (70%+). I want to Normalize it. Given that so many variables are binary, taking values 0 or 1, I am tending towards MinMax Normalization which will render all continuous variables in the [0, 1] range and leave the dummy variables intact. On the contrary Normalized continuous variables will range mostly in the interval [-2, 2] and there is an issue with the dummy variables, whether they should be affected.
So, to conclude, I would appreciate views on the Pros and Cons of the Two Normalization methods --MinMax and Standardization-- in the Setting I described. I say views because perhaps there is no one single correct answer.