I am currently trying to build an algorithm to predict a continuous output (Y) from a list of predictors (X). My first idea was to use a simple linear regression to see how it performs. Distribution of residual errors is not normal.
I have a lot of data and I was wondering if I can take advantage to this to split my training dataset into different datasets where the relationship between X and Y would behave differently. I then would train different linear regressions that would perform better on these subsets. My question is : does it bring a significant improvement and how to split my dataset optimally.
NB: the reason why I want to stick to simple linear regression is that I want to be able to make predictions very quickly.
Thanks in advance