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I work for a medium-sized non-profit. We have a database with approximately 40,000 profiles of folks (mostly patrons). For each show a patron attends, we add an attribute that indicated that they're into that kind of show. For example, if they saw a folk music show, they would get a Folk Music tag.

Does it make sense to use some form of the K-nearest neighbors algorithm to figure out who we could target to purchase tickets? Maybe along with some location data?

For instance, we have about a 100 seat theater, so if we have sold out about half a show, would it be effective to use KNN to try to find other patrons similar to those 50, or is that too small a sample-size?

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This is a classification problem that tries to answer the question: who is likely to buy a ticket to a particular type of show. If you have information about "the previous shows" that each patron attended, you can absolutely use KNN to find which patrons have similar aggregate viewership to a particular type of show.

This might not work if you just have demographic information because there will be a lot of people that share the same demographics. However, as you mentioned, you can use the lat-long or zip code level information to get US census demographic data.

Evaluating the accuracy of this model is a little bit tricky, since you don't know who didn't attend a show due to external factors, but maybe you just want a quick and fast heuristic (which in my opinion sometimes works best).

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