Troubles explaining my results

Thank you for the feedback both.

I work under tight restrictions of GDPR and local laws in Denmark. This, unfortunately, means, that the exact address is not something I can look into. What I can do is find the center of the postal area and make a somewhat vague calculation from there.

I was initially inspired to do this in a Danish healthcare setting after reading this article. I now know that there are a lot of things to improve still. However many of these features would never be OK in the EU

[http://jamesmarquezportfolio.com/walk_through_of_patient_no-show_supervised_machine_learning_classification_with_xgboost_in_r.html]

I mixed this knowledge with a big literature review examining the problem with no-shows, field work and a lot of dataset iterations from EPIC.

As of feature engineering, I’ve done a lot of this to get the features I’ve revealed and I have to limit it now as I have a deadline to keep :slight_smile:

This is only a thesis project that hopefully could get escalated further and I will take ALL of your feedback with me and revisit it.

If you come up with more and better ideas, my head is still on the block for more criticism :wink: it seems that I still have a lot to learn and many concepts to be more knowledgeable on.

Best, Chris

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