  The availability of Martian atmospheric data provided by several Martian
missions broadened the opportunity to investigate and study the conditions of
the Martian ionosphere. As such, ionospheric models play a crucial part in
improving our understanding of ionospheric behavior in response to different
spatial, temporal, and space weather conditions. This work represents an
initial attempt to construct an electron density prediction model of the
Martian ionosphere using machine learning. The model targets the ionosphere at
solar zenith ranging from 70 to 90 degrees, and as such only utilizes
observations from the Mars Global Surveyor mission. The performance of
different machine learning methods was compared in terms of root mean square
error, coefficient of determination, and mean absolute error. The bagged
regression trees method performed best out of all the evaluated methods.
Furthermore, the optimized bagged regression trees model outperformed other
Martian ionosphere models from the literature (MIRI and NeMars) in finding the
peak electron density value, and the peak density height in terms of
root-mean-square error and mean absolute error.
