Graph Data Science with Neo4j: Learn how to use Neo4j 5 library 2.0 and its Python driver for your project

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Unlock the power of your data with Neo4j: the leading graph database for data science and machine learning applications. Key Features Learn how to deal with a graph database Extract meaningful information from graph data Use Graph Algorithms into a regular Machine Learning pipeline in Python Book Description Neo4j and its Graph Data Science Library is a complete solution to store, query and analyze graph data. Graph databases are getting more popular among developers, which means data scientists are likely to face such databases in their future career. Moreover, graph algorithms are a trending topic which enable extracting context information and improve overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and its Graph Data Science Library. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running. Complete with step-by-step explanations of concepts and practical examples. You will begin by querying Neo4j with Cypher and characterize graph datasets. You'll learn how to run graph algorithms on graph data stored into Neo4j, understand the core principles of the Graph Data Science Library to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you will be able to include graph algorithms into your normal ML pipeline. By the end of this book, you will be able to take advantage of the relationships in your dataset to improve your current model and make other types of prediction. What you will learn Querying graph databases such as Neo4j using the Cypher query language Build graph datasets from your own data and public knowledge graphs Extract new kind of features thanks by connecting observations Make graph-specific predictions such as link prediction Build a graph data science pipeline with Neo4j Who This Book Is For Data Scientists and data professionals who have learnt the basics of Neo4j and now want to understand how to build advanced analytics solutions will find this graph data science book useful. Familiarity with the major components of a Data Science project in Python and Neo4J is required. Table of Contents Introducing and Installing Neo4j Using existing data to build a Knowledge Graph Characterizing a Graph Dataset Using Graph Algorithms to Characterize a Graph Dataset Visualizing Graph Data Building a Machine Learning Model with Graph Features Automatically Extracting Features with Graph Embeddings for Machine Learning Building a GDS Pipeline for Node Classification Model Training Predicting Future Edges Writing your custom graph algorithm with the Pregel API

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Pagina's: 288, Paperback, Packt Publishing