Machine learning, concluded: Did the “no-code” tools beat manual analysis?
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I am not a data scientist. And while I know my way around a Jupyter notebook and have written a good amount of Python code, I do not profess to be anything close to a machine learning expert. So when I performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model, I suspected I had done something wrong.
If you haven't been following along thus far, here's a quick review before I direct you back to the first two articles in this series. To see how much machine learning tools for the rest of us had advanced-and to redeem myself for the unwinnable task I had been assigned with machine learning last year-I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.
The whole point of this experiment was to see: