I would like to know how to handle missing data in predictive analysis:
In my case, missing information has been decided not to be omitted, however, certain predictive models such as logistic regression, random forest, couldn't handle missing data. So for this reason I have decided to do some data imputation before modelling.
Like all predictive analysis I have a training set and a test set. My confusion is that when I impute the training data, how can I then be able to handle the test data with possible missing information?