I have a dataset I want to perform multivariate linear regression to it. The dimensions of the dataset are 832085 rows and 11 columns. The data are quite messy and given the size and my lack of experience I am confused on how to clean them. First of all, 6 out of 11 columns have more than 277000 NA values. In that case I know that I cannot remove them because they are a big portion of the dataset so I have to impute them. Usually I would do mean substitution but I've read that this approach might create bias in the data and I wouldn't like to have that. I have tried the packages Amelia and mice in R, mice couldn't run properly and after a while it was giving me an error, Amelia was very fast and completed 5 imputations but it introduced many negative values in the dataset. Anyone with similar experience on that?
Handling many missing values
r;missing data