Best way to impute missing values in a binary variable I'm having a dataset with over 90k records and 28 variables. About 13 of these variables are binary variables and each of these 13 variables have around 40k missing values. Please suggest some imputation techniques that would be appropriate/reliable for binary variables specifically. I tried imputing all these missing values with 0. However the classification model results aren't satisfactory

You would be better off using something like multiple imputation. There is a package in R called mice for this purpose. The imputation will essentially fill in the value that is normally approximated when the variable to be imputed has complete cases with other variables. There is a nice vignette below on the method. However, be warned that unlike a lot if objects in R, mids and mira objects derived from these kinds of packages lose a fair amount of functionality across packages, so be wary ahead of time which functions are easy to work with and which arent.
https://www.gerkovink.com/miceVignettes/
