Machine Learning Question on missing values in training and test data
IMO, if dropping the null values is not an option, you could replace the nulls with the most frequent words.
Just make sure that you do this separately for each set, meaning what words are most frequent in the training set, and what words are most frequent in the test set, as they may differ.
Another option, is to replace the nulls with something like IGNORE TEXT.
I'm training a text classifier for binary classification.  In my training data, there are null values in the .csv file in the text portion, and there are also null values in my test file.  I have converted both files to a dataframe (Pandas).  This is a small percentage of the overall data (less than 0.01).
Knowing this - is it better to replace the null text fields with an empty string or leave it as as empty?  And if the answer is replace with empty string, is it "acceptable" to do the same for the test csv file before running it against the model?