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Find outliers based on users history data using machine learning

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fraudDetectionML

Find outliers based on users history data. I used data.py to generate some random data, saved it on fixtures.py for local testing. You can override the get_data method so you can work with your own data.

How to?

execute the run.py file to see:

  • Blue dots - the train data that used.
  • Green triangles - test data that the algorithm things is a normal user.
  • Red triangles - test data that the algorithm things is NOT a normal user - might be fraud!
  • Orange shape - the decision boundary of the algorithm.

Example of trained classifier

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Find outliers based on users history data using machine learning

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