  KM3NeT, a new generation of neutrino telescope, is currently being deployed
in the Mediterranean Sea. While its two sites, ORCA and ARCA, were respectively
designed for the determination of neutrino mass hierarchy and high-energy
neutrino astronomy, this contribution presents a study of the detection
potential of KM3NeT in the MeV-GeV energy range. At these low energies, the
data rate is dominated by low-energy atmospheric muons and environmental noise
due to bioluminescence and K-40 decay. The goal of this study is to
characterize the environmental noise in order to optimize the selection of
low-energy neutrino interactions and increase the sensitivity of KM3NeT to
transient astrophysical phenomena, such as close-by core-collapse Supernovae,
solar flares, and extragalactic transients. In this contribution, we will study
how using data science tools might improve the sensitivity of KM3NeT in these
low-energy neutrino searches. We will first introduce the data sets and the
different variables used to characterize KM3NeT's response to the environmental
noise. We will then compare the efficiency of various tools in identifying
different components in the environmental noise and in disentangling low-energy
neutrino interactions from the background events. We will conclude with the
implication of low-energy neutrinos for future astrophysical transient
searches.
