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.

The structure of your dataset may lend itself to making this more difficult, if I read your question right.  Staying out of the higher statistics (covered in the other answers) and just in the basic survey/study design realm, you might have three kinds of respondents:

*

*Non Responders - people who did not respond at any point in the survey

*Partial responders - people who responded to some of the years, but not all

*Completes - people who responded to each year in the survey

Each group has some data, hopefully, available about them, even the non responders.  Sometimes, in surveys, you have data from the sample frame, data used to draw the sample, which often includes some demographic information such that you can draw a balanced sample.  That information is available whether they respond or not.  This is only true if the sample was drawn from a known population - not if it was drawn by, say, random digit dialing.  In that case, you may have no information about non responders, but you also would probably not have them in the data file.
The partial responders, people who responded to the initial round of the survey but then later on left (or missed a year but came back later), will have much more information available of course.
Either way, you need a dataset that is respondent level that has all of the starting demographic data you have about these respondents, whether they are nonresponders, partial responders, or completes.  It sounds like your data is not organized this way - so, reorganize it!  This doesn't have to be attached to the rows of data from each year - it can be a separate dataset.
Then, you use this baseline demographic information for whatever imputation you're doing, or for weighting.  The responses to the demographic questions from the first year would be used to impute the later years' variables, including their demographic variables.  You also could design a more complicated model that rolled from year to year - birth predicts 1, 1 predicts 3, 3 predicts 5, and so on.  That would likely be better, but I don't know your data nor your skill level with designing models like this; I'd often err on the side of simpler, as it's more likely I get it right!
I'm not an expert in imputation, so I won't speak to the specific choices - but hopefully this gets you an idea of where to start, and then you can use one of these other great answers to solve your imputation/deleting/etc. problem.
