I've been working my way through the features of the Kaggle House Prices dataset (Note: this is a non-ranking entry, so this is just for exercises), and I've found a couple situations where there is a positive correlation between the feature and the house sale price, but only if the data exists. In one case (shown below) over 10% of the dataset had
null (means "does not apply", not "missing", so I can't fill it in with imputation), but of the non-null values, a scatter plot showed a positive correlation. This looks like a conditionally useful feature and I'd like to keep it, but the nulls are tripping me up.
I experimented and replaced the null values with 0, and when I looked at the scatter plot again, I found that the values that the new 0s spanned the a good chunk of the price range and altered the trend.
Is there a readily available model in sklearn or some other python library that will perform a regression fit only if the data is non-null? If not, would it be best to just drop the column?
Note: This is a small dataset (<1500 entries). This is too small for neural network techniques.