Introduction

Scikit-learn is a well-documented and easy-to-use library that facilitates the application of machine learning algorithms by using simple methods, which ultimately enables beginners to model data without the need for deep knowledge of the math behind the algorithms. Additionally, thanks to the ease of use of this library, it allows the user to implement different approximations (create different models) for a data problem. Moreover, by removing the task of coding the algorithm, scikit-learn allows teams to focus their attention on analyzing the results of the model to arrive at crucial conclusions.

Spotify, a world leading company in the field of music streaming, uses scikit-learn, since it allows them to implement multiple models for a data problem, which are then easily connectable to their existing development. This process improves the process of arriving at a useful model, while allowing the company to plug them into their current app with little effort.

On the other hand, booking.com uses scikit-learn due to the wide variety of algorithms that the library offers, which allows them to fulfill the different data analysis tasks that the company relies on, such as building recommendation engines, detecting fraudulent activities, and managing the customer service team.

Considering the preceding points, this chapter begins with an explanation of scikit-learn and its main uses and advantages, and then moves on to provide a brief explanation of the scikit-learn API syntax and features. Additionally, the process to represent, visualize, and normalize data is shown. The aforementioned information will be useful to understand the different steps taken to develop a machine learning model.