aws-samples/amazon-neptune-ml-use-cases
This repository contains sample code for ML on graph use cases using Amazon Neptune ML
This project helps fraud analysts and risk managers build more accurate fraud detection and recommendation systems. It takes highly connected datasets, like transaction histories or customer interactions, and uses a graph database to identify complex patterns. The output is improved predictions on whether a transaction is fraudulent or a relevant product recommendation.
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Use this if you need to make highly accurate predictions from data with complex relationships, such as identifying fraud rings or personalizing recommendations.
Not ideal if your data is primarily tabular and does not have many interconnected relationships between different entities.
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Jupyter Notebook
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MIT-0
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Last pushed
Dec 14, 2021
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