The regression model is built to predict the values of non-auxetic Poisson's ratio. Cleaning of the dataset is done in a similar way as mentioned previously. The reduced dataset is fed into three different algorithms namely Support Vector, Random Forest, and Gradient Boosting Regression(GBR). The results are shown in figure the other two. We obtain an MAE of 0.019 and R 2 0.82 in predicting the Poisson's ratio for non-auxetic materials. In Figure we have shown the importance scores for the different features using Random Forest Regression. Among the structural variables, the average crystal radius and minimum bond seem to be affecting the Poisson's ratio. While electronic counts like average s,p,d valence electrons, and total valence electron count play an important role in the model. This is verified by the Pearson correlation plot between the Poisson's ratio and other important features in Figure . The features were sorted according to importance scores by RFR(Figure ). Afterwards, we maximized the cross-validated R 2 score to choose the optimal number of features which turns out to be 60. Figure shows the Pearson correlation heat map for the 60 features used in the model. The low inner correlations between features indicate the low redundancy between features In addition to this, the good correlation with the ground truth values dictates that these features are optimal for predicting the Poisson's ratio.