Exercise

Preprocessing within a pipeline

Now that you've seen what steps need to be taken individually to properly process the Ames housing data, let's use the much cleaner and more succinct DictVectorizer approach and put it alongside an XGBoostRegressor inside of a scikit-learn pipeline.

Instructions

100 XP

Import DictVectorizer from sklearn.feature_extraction and Pipeline from sklearn.pipeline.
Fill in any missing values in the LotFrontage column of X with 0.
Complete the steps of the pipeline with DictVectorizer(sparse=False) for "ohe_onestep" and xgb.XGBRegressor() for "xgb_model".
Create the pipeline using Pipeline() and steps.
Fit the Pipeline. Don't forget to convert X into a format that DictVectorizer understands by calling the to_dict("records") method on X.