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Effective Amazon Machine Learning by Alexis Perrier

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Setting up real-time predictions

To demonstrate real-time predictions, we will use the Spam dataset from the UCI repository. This dataset is composed of 5,574 SMS messages annotated spam or ham (non-spam). There are no missing values and only two variables: the nature of the SMS (ham or spam) and the text message of the SMS, nothing else.  The Spam dataset is available at https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection in its raw form, and in the book's GitHub repository at https://github.com/alexperrier/packt-aml/tree/master/ch6. We have simply transformed the target from categorical: spam and ham values to binary: 1 (for spam) and 0 (for ham) so that Amazon ML understands the prediction to be of the binary-classification type. ...

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