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Jul 12 '20 at 5:46 comment added Itamar Mushkin As a start - plug the categorical variables into a one-hot-encoder, normalize all non-binary features (or normalize all with min-max), and see what cosine similarity yields. That's not a magic trick that's sure to work, but it's a start, and it'll help you see where it makes and doesn't make sense. Also, when searching on this site I found your previous question, it has some leads: datascience.stackexchange.com/questions/8681/…
Jul 12 '20 at 5:38 comment added kms There are others. It's a combination of categorical and continuous features.
Jul 12 '20 at 5:30 comment added Itamar Mushkin ... Are these all of your features? It seems to me like almost all of them are categorical
Jul 11 '20 at 14:27 comment added kms This makes sense. I am trying to figure out the most appropriate similarity metric for the data I have. (Updated the question with some sample data). Any thoughts on which distance metric make sense to rank properties by similarity?
Jul 5 '20 at 7:23 history answered Itamar Mushkin CC BY-SA 4.0