Amazon Machine Learning works with CSV files of data. It doesn't appear to have any ability to work with relational data to represent one-to-many relationships.
How should I transform a relational dataset so that it can be used for machine learning?
Would it be best to denormalize the dataset or am I thinking about this the wrong way?
Your best bet would be to denormalize the dataset, so that each input observation has all the attributes (columns) needed to make a prediction. If you can provide a few example data rows, even using made-up data values, I'd be happy to help more.
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