I'm trying to build a regression model but my data-set have many outliers points which I need to analyze and then remove them.
There are two ways to do it,
1) First do all the analysis on every feature without removing anything and then finally remove them at last based on the condition we got from doing the analysis.
2) Do analysis on first feature then remove outliers, then do analysis on second feature then remove outliers.... In this manner one after other.
The insights gained from first method differs from second method.
Which is the correct way?