  We employ a combination of machine learning and first-principles calculations
to predict magnetic properties of rare-earth lean magnets. For this purpose,
based on training set constructed out of experimental data, the machine is
trained to make predictions on magnetic transition temperature (Tc), largeness
of saturation magnetization ({\mu}0Ms), and nature of the magnetocrystalline
anisotropy (Ku). Subsequently, the quantitative values of {\mu}0Ms and Ku of
the yet-to-be synthesized compounds, screened by machine learning, are
calculated by first-principles density functional theory. The applicability of
the proposed technique of combined machine learning and first-principles
calculations is demonstrated on 2-17-X magnets, Ce2Fe17-xCoxCN. Further to this
study, we explore stability of the proposed compounds by calculating vacancy
formation energy of small atom interstitials (N/C). Our study indicates a
number of compounds in the proposed family, offers the possibility to become
solution of cheap, and efficient permanent magnet.
