The release continues, “Neo4j for Graph Data Science combines a native graph analytics workspace and graph database with scalable graph algorithms and graph visualization for a reliable, easy-to-use experience. This framework enables data scientists to confidently operationalize better analytics and machine learning models that infer behavior based on connected data and network structures. Alicia Frame, Lead Product Manager and Data Scientist at Neo4j, explained why Neo4j for Graph Data Science is the most expeditious way to generate better predictions. ‘A common misconception in data science is that more data increases accuracy and reduces false positives,’ explained Frame. ‘In reality, many data science models overlook the most predictive elements within data – the connections and structures that lie within. Neo4j for Graph Data Science was conceived for this purpose – to improve the predictive accuracy of machine learning, or answer previously unanswerable analytics questions, using the relationships inherent within existing data’.”
Keywords: alicia frame • Analytics • any hodler • GDS • graph data science