Use machine-learning to find a family-friendly restaurant


What makes a restaurant child friendy, and how can I use data to predict it? That’s the question I spent the last two months on. As part of my capstone submission to Johns Hopkins University’s Data Science Specialization, I used R to build a a machine-learning prediction algorithm which distinguishes child-friendly restaurants and food-related businesses from child-unfriendly ones using Yelp data. I was able to get a solid accuracy of 89% (9 out of 10 predictions are correct) with an excellent sensitivity of 97% (of 100 child-friendly restaurant recommendations, only 3 are incorrect). That’s enough for a recommendation engine.

In a nutshell, the algorithm says that if you want to enjoy a family outing, pick a place that offers take-out or catering, is inexpensive, has a casual dress code, is non-smoking, known, for great lunches and frequented by groups. On the other hand, if you are out with children, stay away from bars and other places that serve alcohol, venues known for great dinner and late night entertainment, “New American”-style cuisine, expensive, trendy and dressy venues and places that offer street parking and outdoor seating.



Check it out and let me know what you think in the comments!

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