We first used an mRMR algorithm (see methods section) to estimate the importance of all 221 features (see Fig. ) & the supplementary Fig. for all the features). From this initial estimate, we find that 'energy per atom' (a global attribute) is the most important for both bulk and shear modulus. In fact, in general, the global attributes are found to be more relevant to predict bulk modulus compared to shear modulus. This is expected as bulk modulus is strongly related to volume. We find that the structural attributes (F0, F1, F2.) play crucial roles to predict both bulk and shear modulus. Based on the mRMR importance of the features as shown in Fig. ), a subset of 1 st 150 features seems appropriate. Fig. ) illustrates the predictability (mean absolute error (MAE)) of our ML models for bulk and shear moduli depending on the size of the training dataset, using our selected subset of 150 features. The results of five independent trials reveal that, for bulk modulus, MLP performs poorly with a smaller amount of data, but it exhibits a significant improvement as the size of the training dataset increases. Among all models, overall, KNN is the worst, except for the LR models (refer to supplementary information 4. (a)), while all other models consistently improve with increasing training data. Moreover, we see that the standard deviation of the accuracies of GBM, KRR, RF, GPR, and SVR is less than ~1.5 GPa, indicating a consistent performance. A similar trend is observed for shear modulus predictions. Overall, the performance of GBM, KRR, RF, GPR, and SVR are nearly identical.