How to handle NaNs for ratio feature for binary classifier? I'm creating a churn model and would like to create a ratio (# customers / total transaction) for each merchant. About 70% of the data are NaNs (zero/zero). 

I was wondering what I should impute for the 70% of the NaNs. I have other features and I don't like to delete the 70% of the data. 

But if I impute 0s, the distribution would probably become different from ground truth since lower number means large transaction volume with a few customer. If I impute mean though, it'll also be different since the element (zero / zero) has no actions fundamentally. 

I was about to impute -1 to distinguish the NaNs with non-NaNs. Would that make sense for a feature of binary classifier? 

Thanks!
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.