I am a student working on a project whose main subject is variable selection through methods such as AIC stepwise regression, Ridge regression and LASSO regression using R.
My aim is to build a simple and efficient model which tries to predict the response given a data set which contains values for 44 predictor variables.
Before starting my modelling, I wanted to know if transformations are needed and if outliers need to be removed. As there are many covariates, I don't have time to go through each case exhaustively, so, being my first time working on this kind of projects, I don't know what to do.
Is there any simple and justifiable way to assess the need for a transformation or outlier removal in these cases? Are there any commonly used rules of thumb?
Any other suggestion or comment is highly appreciated, thank you very much.