In this chapter, we extracted significant meanings 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 pipeline. This was achieved by encapsulating a train/cross-validation/test setting into our hypothesis, which was expressed in terms of various activities – from data selection and transformation to the choice of learning algorithm and its best hyperparameters.
In the next chapter, we will delve into the principal machine learning algorithms offered by the Scikit-learn package, such as linear models, support vectors machines, ensembles of trees, and unsupervised techniques for clustering, among others.
