I have gone through https://stackoverflow.com/questions/46120727/replace-missing-values-in-categorical-data regarding handling missing values in categorical data.
Dataset has about
6 categorical columns with
missing values. This would be for a binary classification problem
I see different approaches where one is to
just leave the missing values in category column as such, other to impute using
from sklearn.preprocessing import Imputer, but unsure which is better option.
In case if
imputing is better option, which libraries could I use before applying the model like
LR,Decision Tree, RandomForest.