(This is not a question on ways to handle missing data)
I have a dataset with around $80$ or so features and around $100000$ rows. Several of those features have missing (NULL) values for a "large proportion" (more than 20%) of the records.
Is there a general way to decide the % of the total rows for which a feature has to be missing, so that we can consider it useless and remove it from our analysis? Or does this depend on the context? Indirectly, I want to know the % of missing values for a feature so that imputation is meaningless.