I am doing regression modeling, where I have a continuous value I want to predict using many (~60) features.
However, many of the samples in my dataset contain NaN
features. Some features are very reliable, but other features may be significantly or mostly NaN
for all samples.
I want to use the reliable features I do have to impute the missing values of the non-reliable ones. I don't want to simply replace the missing values naively with some number (like the mean, or median for instance) like scikit
impute offers.
But no package I can find in python (except maybe fancyimpute
) will do this. Yet there are many packages I can find in R that will do this.
Should I send my data to R, or are there other python packages I don't know about for smart imputation? In general, what are some good environments and packages that could make it easy to start doing this? Where should I be directing my efforts.