Data were scaling. In the case of the artificial neural network, additional scaling was performed to improve the training process using a min-max scaler from 0 to 1. This process is because scaled **data** tend to improve training, given the high number of matrix multiplications by the nature of the artificial neural network. This characteristic is the main difference between the input of both models. On the other hand, the KNN algorithm classifies information based on different types of distances: Euclidean distance, cosine squared distance, etc. Moreover, the scaling of **data** does not represent any difference.