SETTING UP A SANDBOX ENVIRONMENT

 

Now that you’re familiar with the theoretical framework of recommender systems, it’s time to turn our attention to building a prediction model. For the exercises presented in this book, we’ll be using the programming language Python, which is quick to learn and popular among data scientists.

If you don't have programming expertise or any experience coding in Python, don’t worry. The key purpose of these exercises is to understand the methodology and steps behind building a basic recommender system. As for our development environment, we’ll be installing Jupyter Notebook, which is an open-source web application that allows for the editing and sharing of code notebooks. As a convenient and user-friendly workspace, notebooks allow you to segment code into chunks (that can run separately) and add formatted text and headings to annotate your code.

The remainder of this chapter is dedicated to installing Jupyter...