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This repository provides all the code associated with the book titled "Machine Learning for the Quantified Self", authored by Mark Hoogendoorn and Burkhardt Funk and published by Springer in 2018. The website of the book can be found on Both R code, Python 2 code (used to generate the results in the book) and Python3 code can be found (due to updated packages, results might differ a bit compared to those reported in the book). For the Python3 code, both a Docker setup and a requirements file are available, see

Note that we have tried to make the code as robust as we can, but we cannot provide any guarantees on its correctness. This code is made available under the GNU public license. We have used snippets of code from other sources and have tried to add references to these in our code where possible. When using the code for publications, please include a reference to the book in your paper:

Hoogendoorn, M. and Funk, B., Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data, Springer, 2018.


Code belonging to the book machine learning for the quantified self.



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