I know that there have been similar questions but most of them have not worked, this is why I start this question.
I have a very large dataset (around 2,500,000 records) with approximately 100 variables, most of which are categorical and some of them numerical. There are a lot of missing values which are probably MCAR/MAR (the data come from an automated log file). Also, some categorical variables have a lot of different values (e.g. city attribute). I am trying to perform feature selection to find the most informative variables with regard to a discrete numerical target variable.
I have two problems:
i) How to fill in the missing values: most of the standard packages' functions get stuck (i.e. mice, hmisc). Should I use just the mode/median or fill in some default "unknown" value? If I decide to take a subset of the rows to decrease the size, how small should this subset be - if there are any empirical rules?
ii) Most of the feature selection techniques I have tried (fscaret, fselector, boruta) after using some simple imputation technique either hang or give me an error. I believe this has to do with the fact that some categorical variables have many possible values. What are some practical ways to deal with this?