  Over the last years, Machine Learning (ML) tools have been successfully
applied to a wealth of problems in high-energy physics. In this work, we
discuss the extraction of the average number of Multiparton Interactions
($\langle N_{\rm mpi} \rangle$) from minimum-bias pp data at LHC energies using
ML methods. Using the available ALICE data on transverse momentum spectra as a
function of multiplicity, we report that for minimum-bias pp collisions at
$\sqrt{s} =$ 7 TeV the average $N_{\rm mpi}$ is 3.98 $\pm$ 1.01, which
complements our previous results for pp collisions at $\sqrt{s} =$ 5.02 and 13
TeV. The comparisons indicate a modest energy dependence of $\langle N_{\rm
mpi} \rangle$. We also report the multiplicity dependence of $N_{\rm mpi}$ for
the three center-of-mass energies. These results are qualitatively consistent
with the existing ALICE measurements sensitives to MPI, therefore they provide
additional experimental evidence of the presence of MPI in pp collisions.
