  We introduce machine learning methodology to the study of lattice polytopes.
With supervised learning techniques, we predict standard properties such as
volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on
2d polygons and 3d polytopes with Pl\"ucker coordinates as input, which
out-perform the usual vertex representation.
