Machine Learning can help you find fraudulent claims in insurance data. But to get accurate outcomes, you need to prepare the data.
Join this session to learn how to use SQL to extend the input data, ML algorithms to analyze it, and understand the results using Attribute Importance.
The Iron kingdom has recently had several horses injured, dragons killed and cattle go missing, but something in the data doesn’t quite add up! Could there be someone trying to claim insurance money that they shouldn’t be?
This session will show you how to use SQL can bring context to data enabling better analysis during an investigation. We will cover how this context can be used to enhance and improve machine learning outcomes.
Finally, we will investigate the use of Attribute Importance, to show how this can enable business users to understand the results from the Machine Learning algorithms alongside the use of SQL context. The demonstration will be implemented in Autonomous Datawarehouse and Oracle Analytics Cloud.
This session is for developers, data scientists, and analysts searching or complex patterns in data.