Is there any specific cutoff / limit /percentage for the acceptance of missing values in a particular variable in a dataset?
Ex : If 70% of the values are missing in a variable, what action should we take… delete the variable according to its importance in the model or impute with different methods???
In my case, i have a dataset with 80 variables and out of that 10 variables have more than 50% of values missing.
Could anyone suggest a correct approach for this problem.