Putting it all together with pipelines

Now that we've used pipelines and data transformation techniques, we'll walk through a more complicated example that combines several of the previous recipes into a pipeline.

Getting ready

In this section, we'll show off some more of pipeline's power. When we used it earlier to impute missing values, it was only a quick taste; here, we'll chain together multiple pre-processing steps to show how pipelines can remove extra work.

Let's briefly load the iris dataset and seed it with some missing values:

from sklearn.datasets import load_iris
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
iris_data = iris.data
mask = np.random.binomial...