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Hardness, or the quantitative value of resistance to permanent or plastic deformation, plays a crucial role in materials design for many applications, such as ceramic coatings and abrasives. Hardness testing is an especially useful method because it is nondestructive and simple to implement and gauge the plastic properties of a material. In this study, I proposed a machine, or statistical, learning approach to predict hardness in naturally occurring ceramic materials, which integrates atomic and electronic features from composition directly across a wide variety of mineral compositions and crystal systems. First, atomic and electronic features, such as van der Waals, covalent radii, and the number of valence electrons, were extracted from composition. The results showed that this proposed method is very promising for predicting Mohs hardness with F1-scores >0.85. The dataset in this study included modeling across a larger set of materials and hardness values, which have never been predicted in previous studies. Next, feature importances were used to identify the strongest contributions of these compositional features across multiple regimes of hardness. Finally, the models that were trained on naturally occurring ceramic minerals were applied to synthetic, artificially grown single crystal ceramics.