ML Introduction with scikit-learn

In the previous section, we completed three preprocessing steps, Imputing, Encoding and Scaling.

We did it step by step, transforming the needed columns and collecting them back to the X array.
It is a tedious process, especially when there is an OneHotEncoder that changes the number of columns.
Another problem with it is that to make a prediction, new instances should go through the same preprocessing steps, so we would need to perform all those transformations again.

Luckily, Scikit-learn provides a Pipeline class – a simple way to collect all those transformations together, so it is easier to transform both training data and new instances.

Pipeline is a container for all the transformers (and the final estimator, as you will see later).
By calling the .fit_transform() method of a Pipeline object, it will sequentially call each transformer's .fit_transform().
This way, you only need to call .fit_transform() once to transform a training set and then the .transform() method to transform new instances.