The pressure is the most essential feature according to the results obtained by , especially at higher values where the solubility of the DES increases. In Figure , the results for extrapolation at lower pressures are shown. When lower pressures are absent in the training dataset, significant error deviations occur from 26.3 to 100 kPa. At higher pressure values, the prediction values error decreases. Under these observations, the model can predict with high accuracy when the pressure is near the upper bound in the training data. A threshold of 256 kPa was used, representing -20 % of 320.1 kPa. The filtered results show that the model performance increases, and the resulting metrics were 0.96, 1.60%, 0.07, and 0.12 for the R 2 , MAPE, MAE, and MSE, respectively. Extrapolation at higher values, shown in Figure , also has high deviations for the test dataset, but are lower compared with the results obtained by extrapolating at lower pressures. A threshold of 886 kPa was defined, +20% higher than the lower limit, and the R 2 , MAPE, MAE, and MSE improved to 0.94, 4.64%, 0.12 and 0.17, respectively.