The number of epochs was also monitored to ensure the model convergence and avoid overfitting issues. Based on this, Mean Squared Error (MSE) for both training and test was plotted as a function of the number of epochs, as shown in Figure . The plot of Figure shows that the MSE decreases steadily until the maximum number of epochs, set at 5000. Beyond this point, the validation MSE could show significant noise, potentially harming the prediction of unseen data. Thus, setting 5000 epochs and adding an early stop function aids the model's convergence without significant noise, preventing overfitting.