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For me, this was an exercise in practicing machine learning and I found it very interesting that you could get 80% accuracy with such few features. A look up table works very well, but if a name does not exist in the table, you could possibly use some kind of ML to guess the gender.

As soon as a name is not present in the database, the approach based on machine learning is useful.

It is very likely that the name absent from the db will have features that reveal its gender.

So 1) try db lookup, if absent 2) use a classification algorithm (which will always provide an answer)

A non-software-developer and a developer need shopping lists for their next trip to the supermarket. Developer: "I know! First we'll set up Postgres, then hook up the controller logic... Oh, we'll need a front end that works well on both desktop and mobile..." The non-developer grabs a pen and paper and is at Safeway in 5 minutes.