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 Country
, Date of birth
, and Revenue
. None of them with relevant correlation with Gender
.
What is the best way to handle these NaN
s?
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.
Thanks.