  In this study, we attempted to model vacancy ordered half Heusler compounds
with 18 valence electron count (VHH) derived from 19 VEC compounds such as
TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb
with semiconducting behavior. The main motivation is that such a
vacancy-ordered phase not only introduces semi conductivity but also it
disrupts the phonon conducting path in HH alloys and thus reduces the thermal
conductivity and as a consequence enhances the thermoelectric figure of merit.
In order to predict the formation energy ({\Delta}Hf) from composition and
crystal structure we have used 4684 compounds for their {\Delta}Hf values are
available in the material project database and trained a machine learning model
with R2 value of 0.943. Using this trained model, we have predicted the
{\Delta}Hf of a list of VHH. From the predicted database of VHH we have
selected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning prediction
using accurate DFT calculation. The calculated {\Delta}Hf for these two
compounds from DFT calculation are found to be comparable with our ML
prediction. The calculated electronic and lattice dynamics properties show that
these materials are narrow band gap semiconductors and are dynamically stable
as their all-phonon dispersion curves are having positive frequencies. The
calculated Seebeck coefficient, electrical conductivity as well as thermal
conductivity, power factor and thermoelectric figure of merit are analyzed.
