Can I delete missing data? I have a dataset (4,898 X 17,000) that follows 4898 mothers, fathers, and their children over a period of 15 years. The interviews have been conducted at baseline (when the child was born), year-1, year-3, year-5, year-9, and year-15. I want to predict GPA (at year-15) using random forest from a set of features (Gender, household income, household education, Parental involvement in studies, parental expectation, family structure, cognitive and non-cognitive variables. All of these variables except cognitive variables have been captured at year-15. Cognitive variables have been captured at year-9. Year-9 has about 1300 values missing whereas year-15 has about 1454 values missing due to non-response in year-9 and 15. I am quite new to imputation and I am not sure how to use multiple imputations here (especially when there are more than 1000 rows that have missing values for all the columns). Any help in this regard would be very helpful.

It's not a good a idea at all deleting missing data using tree-base models (if you're deleting the rows with at least one missing data) it'd be better you just ignore missing data when you're splitting a node on a decision tree because this model will just ignore the missing values unlike ignoring the whole instance (where there may be so many non-missing values since you got to much columns )
