74 – ETL and Supervised ML using Python

• Amey Mahajan, Enterprise Presale Engineer, Neo4j
• Alexander Fournier, Enterprise Presale Engineer, Neo4j
Session type: Full Length Session

Abstract: This presentation will be partitioned into two parts: 1) ETL best practices using the Neo4j Python driver and 2) Running supervised ML with the Neo4j Graph Data Science Library using the Python client. Part 1 (ETL): The ETL portion of the presentation will cover building Neo4j property graphs using the Python driver. We’ll also go over best practices, including batching, transaction functions, templatized Cypher, and more. Part 2 (Supervised ML): The supervised ML portion will explore using the Python graph data science client. You can specify all the different properties, configurations, and user inputs that will be used to run node classification algorithms and return the results. The function calls are modular and allow the user to quickly build a graph data science pipeline and get results for any of the three node classification algorithms (fastRP, Node2Vec, GraphSage).