How to handle NaNs for ratio feature for binary classifier?
The usual solution is a mix of a few methods :


remove these records, but this should be avoided if possible
encode it based on general business knowledge, you could pick a
distinct number ( extremely large or small ) and then train the
model on it.
finally, I like usually like to plot one-way charts and
impute with values with the dependent (say churn rate) close enough
to the observed dependent rate over the NaN values for that
variable.
Edit - more recently, I have been using the package MICE and think it  could be used for imputing when you cannot do point 2 and there are far too many variables to do point 3 individually.