I have a dataset of around 5,500 observations.
One of the variables is
Gender for which at least 25% of the observations are missing.
Dropping the missing values seems a bit brute, however I have not found a good way of interpolating binary data.
Other variables of the data are
Date of birth, and
Revenue. None of them with relevant correlation with
What is the best way to handle these
I was thinking of using a logistic regression function with
Gender as the target and the rest of the variables as the predictors, but I am not sure whether this is a good choice.