I second the advice.

Let me brag a bit. Once in a friendly discussion the following question came up: How to predict for an unknown first name whether it is a male or female name. This was in a context of Hungarian names, as all of us were Hungarians. I had a list of Hungarian first names in digital format. The discussion turned into a bet: I said I can write a program in half an hour that tells with at least 70% precision the sex of a first name it never saw before. I am quite fast with writing small scripts. It wasn't even close: It took me 9 minutes to

• split my sets of 1000 male and 1000 female names into a random 1000-1000 train-test split,
• split each name into character 1,2- and 3-grams. E.g.: Luca was turned into ^L u c a$ ^Lu uc ca$ ^Luc uca$.
• feed the training data into a command line tool to train a maxent model,
• test the accuracy of the model on the unseen test data.

The model reached an accuracy of 90%. In retrospect, this is not surprising at all. Looking into the linear model, the most important feature it identified was whether the name ends with an 'a'. This trivial model alone reaches some 80% precision for Hungarian names, so if I knew this in advance, I could have won the bet in 30 seconds instead of 9 minutes, with the sed command s/a$/a FEMALE/.