Real numbers, data science and chaos: How to fit any dataset with a single parameter

We show how any dataset of any modality (time-series, images, sound...) can
be approximated by a well-behaved (continuous, differentiable...) scalar
function with a single real-valued parameter. Building upon elementary concepts
from chaos theory, we adopt a pedagogical approach demonstrating how to adjust
this parameter in order to achieve arbitrary precision fit to all samples of
the data. Targeting an audience of data scientists with a taste for the curious
and unusual, the results presented here expand on previous similar observations
regarding expressiveness power and generalization of machine learning models.