This is the second episode in our exploration of "no-code" machine learning. In our first article, we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science. If you haven't read that article, you should go back and at least skim it. If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and then throw that all away and hit the "easy" button. The two fields missing from the Hungarian data seem potentially consequential, but the Cleveland Clinic data itself may be too small a set for some ML applications, so we'll try both to cover our bases.

If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and …