Second, the mutable architecture of NN-based models allows them to modify their framework during training, optimizing alignment with the inherent data distribution. Lastly, given abundant data, NN-based models excel in discerning subtle data patterns because of their proficiency in processing high-dimensional input attributes, whereas regression-based models may have tendencies of underfitting. This proficiency of NN-based models is further enhanced by the use of techniques such as grid search for hyperparameter optimization, particularly crucial in fine-tuning the performance of NN-based models because of their complex architectures and the numerous parameters required .