Improve ML Predictions using Connected Feature Extraction

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The most practical way to improve our machine learning predictions right away is using graph algorithms for connected feature extraction. We’ll quickly dive into creating a machine learning pipeline and tips on training and evaluating a model for link prediction – integrating Neo4j and Spark in our workflow. We’ll look at an example using several models to predict future collaborations and show measurable improvements using graph based features.


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About Amy E. Hodler

Amy manages the Neo4j graph analytics programs and marketing. She loves seeing how our ecosystem uses graph analytics to reveal structures within real-world networks and infer dynamic behavior. Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft and Hewlett-Packard (HP). She most recently comes from Cray Inc., where she was the analytics and artificial intelligence market manager. Amy has a love for science and art with an extreme fascination for complexity science. When the weather is good, you’re likely to find her cycling the passes in beautiful Eastern Washington.