This chapter gave an overview of classical data science pipelines and how to integrate graph data into them. Thanks to the Neo4j Python driver, you are now able to import Neo4j data into a pandas DataFrame, which can then be used as usual in any other applications, such as model training with scikit-learn. You have also learned how to programmatically run a graph algorithm from the GDS and use the result as a new type of feature for your model.
In the following chapters, we will continue our journey through graph analytics. In this chapter, we stuck to classical machine learning methods such as decision trees. We will now go on to learn how the graph structure can be used to answer different kinds of questions, starting with the link prediction problem, which we are going to tackle in the next chapter.