I'm learning using R to build predictive models recently by myself and have many questions on how to attack a question. I'm given a data set of 8000 observations with 300 features. My goal is to build a predictive model to predict a target column of continuous values. I'm not given where the data is coming from, nor the actual meaning of the features (they are listed just as f1 to f300). 5 features are categorical. Almost all features have some missing values. Only 20 observations are complete cases. I have several questions:
How to deal with the missing data? Would either MICE or Amelia package in r for multiple imputation be a good choice, but then how to deal categorical missing data?
Should I do data imputation before building a predictive model or the other way around?
Would random forest be a good choice in this case? How many features should be included in the decision tree?
Thanks in advance for any suggestions.