I have a classification problem with about 10,000 records. I have twenty predictors and I have data for most of the predictors.
Some of the predictors provide valuable information, but I only have data on some of the rows. The model has to do with user behavior and the data is structurally missing, since it is information that has only been added in the last couple years (out of a sample set that references five years of behavior).
The missing data ranges from 1% missing on the low end to 85% missing on the high end. Most of the variables with missing data are missing on 50%-60% of records.
I figure that I can predict the data for records with less than 15% missing and that I can drop the variables for records with more than 80% missing.
What do I do about the variables in between? All the variables with missing data are categorical. Would I add bias if I made an "Unknown" category for each of them?