Neural networks have their place, but are probably the most complicated and opaque machine learning tool. They are also hard to set up: so many parameters! Given that, I found it really strange that they went straight for a neural network (and then implemented one themselves!). Surely the place to start would be Naive Bayes, followed up with regularized logistic regression (through something like glmnet). Heck, even random forests would do quite well on this task I imagine, although thats getting closer to on the complexity and opaqueness spectrum towards NN.

There is also no evidence of doing cross-validation, and in another comment they say they used entire data set to do variable selection - a pretty bad mistake. They justify by saying they aren't in an academic environment, but thats kind of a bad excuse, as given the way they've done it I'm very unsure whether they actually are getting the accuracy they think they are.

I also worry that they sunk two man-months into this when they could probably have achieved similar if not better results with off-the-shelf and battled-tested tools. Sets off a lot of warning bells.