  Neutron stars (NS) are compact objects with strong gravitational fields, and
a matter composition subject to extreme physical conditions. The properties of
strongly interacting matter at ultra-high densities and temperatures impose a
big challenge to our understanding and modelling tools. Some difficulties are
critical, since one cannot reproduce such conditions in our laboratories or
assess them purely from astronomical observations. The information we have
about neutron star interiors are often extracted indirectly, e.g., from the
star mass-radius relation. The mass and radius are global quantities and still
have a significant uncertainty, which leads to great variability in studying
the micro-physics of the neutron star interior. This leaves open many questions
in nuclear astrophysics and the suitable equation of state (EoS) of NS.
Recently, new observations appear to constrain the mass-radius and consequently
has helped to close some open questions. In this work, utilizing modern machine
learning techniques, we analyze the NS mass-radius (M-R) relationship for a set
of EoS containing a variety of physical models. Our objective is to determine
patterns through the M-R data analysis and develop tools to understand the EoS
of neutron stars in forthcoming works.
