Feature Engineering Made Easy

Creating our pipeline

To review, we have transformed the columns in our dataset in the following ways thus far:

• boolean, city: dummy encoding
• ordinal_column: label encoding
• quantitative_column: ordinal level data

Since we now have transformations for all of our columns, let's put everything together in a pipeline. 

Start with importing our Pipeline class from scikit-learn:

from sklearn.pipeline import Pipeline

We will bring together each of the custom transformers that we have created. Here is the order we will follow in our pipeline:

1. First, we will utilize the imputer to fill in missing values
2. Next, we will dummify our categorical columns
3. Then, we will encode the ordinal_column
4. Finally, we will bucket the quantitative_column

Let's set ...