Full disclosure: This is a copy-and-paste of my [answer](https://stats.stackexchange.com/a/518534/66864) to a [question](https://stats.stackexchange.com/q/73801/66864) over on CrossValidated (stats Stack Exchange).

See the 2019 preprint [Machine Learning meets Number Theory:
The Data Science of Birch-Swinnerton-Dyer](https://arxiv.org/abs/1911.02008) by Alessandretti, Baronchelli & He. Here is the Abstract:

>Empirical analysis is often the first step towards the birth of a conjecture.
This is the case of the Birch-Swinnerton-Dyer (BSD) Conjecture describing
the rational points on an elliptic curve, one of the most celebrated unsolved
problems in mathematics. Here we extend the original empirical approach, to
the analysis of the Cremona database of quantities relevant to BSD, inspecting
more than 2.5 million elliptic curves by means of the latest techniques in data
science, machine-learning and topological data analysis.

> Key quantities such as rank, Weierstrass coefficients, period, conductor,
Tamagawa number, regulator and order of the Tate-Shafarevich group give rise
to a high-dimensional point-cloud whose statistical properties we investigate.
We reveal patterns and distributions in the rank versus Weierstrass coefficients,
as well as the Beta distribution of the BSD ratio of the quantities. Via gradient
boosted trees, machine learning is applied in finding inter-correlation amongst
the various quantities. We anticipate that our approach will spark further research on the statistical properties of large datasets in Number Theory and
more in general in pure Mathematics.