I am making my classification project and I have this situation after using seaborn heatmap.
Column 0 is my target, where I have data with 3 classes. To my knowledge I should remove column highly correlated with target value. My questions are:
Should I remove also features highly correlated with features different than target? For example I can see very high correlation between column 27 and 46. Should I remove one of them?
This is correlation heat map from my whole dataset. Should I examine correlation and consider dropping columns in this way or should I make it only for train set and test set left without dropping any column? Logic dictates that making this transformations is better only on train set, but I prefer to be sure.