Plot The Scatterplot Matrix

Lesson 7 of 15

The Diagnosis column is a boolean label, either 0 for healthy patients or 1 for sick patients. So, for the scatterplots, we should only plot features with high cardinality (= having lots of discrete values). That will produce nice plots where we can hopefully spot some linear relationships.

The high-cardinality columns in the Cleveland CAD dataset are Age, RestingBloodPressure, Cholesterol, MaxHeartRate and STDepression.

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