I'm working with a dataset where the dependent variable is continuos (sale price of houses) and there a couple dozen features I'm using to predict the sale price using a linear regression model. These features include binary dummy variables, categorical, and continuous variables - all on different scales.
The dependent variable (sale price) is skewed, so I've instead created a new feature that is
log(salePrice) so the distribution is centered. My question is, I had planned on using SckiKit-Learn's StadardScaler class on the explanatory features. Does it make sense to use two different preprocessing techniques, or should I simply use the
log of all the explanatory features like I do with the dependent variable?