The temperature was detected as a key feature by and is highly important at lower values where high solubility is achieved. For this reason, extrapolation to lower values is of high importance. A target test size was fixed to 30%. However, the range distribution of the temperature has discrete values, resulting in a test size of 39% in a range of 293.15 to 303.15 K, and for the training set, 308.0 to 348.15 K. From the test dataset, the points of molecules and pressure that were not presented in the train set were excluded. With these isolated points, the performance of the model extrapolating to temperatures not contained within the training phase can be evaluated. The remaining points after the filtering were 15.53% of the original test size, as shown in Figure . The isolated points were predicted with high accuracy, implying an excellent performance of the model to extrapolate at lower temperature values.