Before training, it was necessary to create methods to preprocess the experimental data to improve the model's convergence. For this specific work, two steps were followed. In the first step, KFold cross-validation with 5 folds was employed to partition the data into training and test sets. This technique involves randomizing the data while maintaining an 80-20 split ratio. By training the model on different configurations of the same dataset, we aimed to evaluate its robustness. This cross-validation method can be found in the scikit-learn library .