12 – Integrated Graph Machine Learning with GDS 2.0 and Python

18 Jun, 2022

Speaker: Sean Robinson, Lead Data Scientist, Graphable Session type: Lightning Talk Abstract: One of the toughest challenges for data scientists adopting Neo4j Graph Data Science is unfamiliarity with Cypher and the Neo4j interface. In this demonstration, we will break down this barrier by demonstrating how to integrate Graph Data Science with Python analytics in Jupyter. Using the GDS 2.0 Python driver, we will work through a graph machine learning use case via Python in Jupyter. We will then integrate and interpret the results using other Python libraries to demonstrate how the Neo4j Python driver offers seamless integration with the tools and libraries data scientists use in their daily work. You will get access to sample code for performing ML and graph analytics in GDS using nothing but Jupyter, Python, and simple Cypher. You will also learn how to integrate your results with popular data science libraries.

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