  Using the data set of The Three Hundred project, i.e. 324 hydrodynamical
resimulations of cluster-sized haloes and the regions of radius 15 $h^{-1}$Mpc
around them, we study galaxy pairs in high-density environments. By projecting
the galaxies' 3D coordinates onto a 2D plane, we apply observational techniques
to find galaxy pairs. Based on a previous theoretical study on galaxy groups in
the same simulations, we are able to classify the observed pairs into "true" or
"false", depending on whether they are gravitationally bound or not. We find
that the fraction of true pairs (purity) crucially depends on the specific
thresholds used to find the pairs, ranging from around 30 to more than 80 per
cent in the most restrictive case. Nevertheless, in these very restrictive
cases, we see that the completeness of the sample is low, failing to find a
significant number of true pairs. Therefore, we train a machine learning
algorithm to help us to identify these true pairs based on the properties of
the galaxies that constitute them. With the aid of the machine learning model
trained with a set of properties of all the objects, we show that purity and
completeness can be boosted significantly using the default observational
thresholds. Furthermore, this machine learning model also reveals the
properties that are most important when distinguishing true pairs, mainly the
size and mass of the galaxies, their spin parameter, gas content and shape of
their stellar components.
