In this chapter, we will take what you have learned about graphs, graph databases, and the different types of information that can be extracted from graph structures (node importance, communities, and node similarity) and learn how to integrate this knowledge into a machine learning pipeline to make predictions out of data. We will start by using a classical CSV file, containing information from a questionnaire, and recap the different steps of a data science project using this data as the central theme. We will then explore how to transform this data into a graph and how to characterize this graph using graph algorithms. Finally, we will learn how to automate graph processing using Python and the Neo4j Python driver.
The following topics will be covered in this chapter:
- Building a data science pipeline
- The steps toward graph machine...
