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I'm looking at some data. Actually, the Boston Housing dataset is probably a good proxy for it: https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

I'm wondering if there's a way to predict if I got a "good price" given certain conditions. So something like, if I'm given a tuple such as CRIM, ZN, INDUS = (0.006320,18, 2.31), then is a house price of 50 significantly higher or lower than expected?

This isn't quite vanilla anomaly detection, because the combination of a particular CRIM, ZN, INDUS may be rare and I don't want to flag it unless price is out-of-spec for that. I guess the question is given a combination of (CRIM, ZN, INDUS) (no matter how anomalous that is), is the combination of (CRIM, ZN, INDUS, Price) rare? So, it's like a naiive bayes' problem, but that only works with categorical outputs

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Yes, fit a regression equation ( or deep NN) to predict price based on all other parameters. then calculate error ( actual- predicted) , positive error indicates higher price than expected( what is should be) and vice versa. get a basic idea of types of anomalies-

https://machinelearningstories.blogspot.com/2018/07/anomaly-detection-anomaly-detection-by.html

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