Preparing data for a chosen machine learning algorithm is called feature engineering and is a crucial part of the machine learning life cycle. Feature engineering is done in different stages or phases. The multi-stage processing code used to process data is collectively known as a data pipeline. Making a data pipeline using standard processing steps, wherever possible, makes it reusable and decreases the effort needed to train the models. By using more well-tested software modules, the quality of the code is also enhanced.

Let's see design a reusable processing pipeline for the classifiers challenge. As mentioned, we will prepare data once and then use it for all the classifiers.