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I am currently working on a solution to get the type of food served in a database with 10k restaurants based on their description. I'm using lists of keywords to decide which kind of food is being served.

I read a little bit about machine learning but I have no practical experience with it at all. Can anyone explain to me if/why it would a be better solution to a simple problem like this? I find accuracy more important than performance!

simplified example:

["China", "Chinese", "Rice", "Noodles", "Soybeans"]

["Belgium", "Belgian", "Fries", "Waffles", "Waterzooi"]

a possible description could be:

"Hong's Garden Restaurant offers savory, reasonably priced Chinese to our customers. If you find that you have a sudden craving for rice, noodles or soybeans at 8 o’clock on a Saturday evening, don’t worry! We’re open seven days a week and offer carryout service. You can get fries here as well!"

1 Answer

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To solve your problem, here are the steps you should do:

  1. Create a feature extractor - that given a description of a restaurant, returns the "features" (under the Bag Of Words model explained above) of this restaurant (denoted as an example in the literature).
  2. Manually label a set of examples, each will be labeled with the desired class (Chinese, Belgian, Junk food,...)
  3. Feed your labeled examples into a learning algorithm. It will generate a classifier. From personal experience, SVM usually gives the best results, but there are other choices such as Naive BayesNeural Networks and Decision Trees, each has its own advantage.
  4. When a new (unlabeled) example (restaurant) comes - extract the features and feed it to your classifier - it will tell you what it thinks it is (and usually - what is the probability the classifier is correct).

For a training point of view, check out the SVM Algorithm Tutorials. Also, Neural Network Tutorial would also be one of the better procedures in understanding various 

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