In this chapter, we extracted significant meaning from data by applying a number of advanced data operations—from EDA and feature creation to dimensionality reduction and outlier detection.

More importantly, we started developing, with the help of many examples, our data science pipeline. This was achieved by encapsulating into a train/cross-validation/test setting our hypothesis that was expressed in terms of various activities—from data selection and transformation to the choice of the learning algorithm and its best hyper-parameters.

In the next chapter, we will delve into the principal machine learning algorithms offered by the Scikit-learn package, such as, among others, linear models, support vectors machines, ensembles of trees, and unsupervised techniques for clustering.