# Percentage of missing values so that we can't perform imputation

(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.

• You can just throw them away as such, fill them using sk-learn's imputer strategy seprately on train and test sets.... If you have a feature having nearly 90-95% missing values, then we (can) probably throw them away as People had done the same in Kaggle's Titanic Competition for the Drivers'Cabin – Aditya Apr 2 '18 at 14:47