In the previous chapter, we learned how to manage the digital data of customers. We also covered the Open Bank Project and the Open Bank API. In addition, we learned about document layout analysis and looked at an example of projecting the cash flow for a typical household. Then, we looked at another example of how to track daily expenses using invoice entity recognition.
In this chapter, we will learn how to combine data from a survey for personal data analysis. We will learn techniques such as Neo4j, which is a graph database. We will build a chatbot to serve customers 24/7. We will also learn how to predict customer responses using NLP and Neo4j with the help of an example. After this, we will learn how to use cypher languages to manipulate data from the Neo4j database.
The following topics will be covered in this chapter:
- Financial concepts of...
