Knowing how to deal with categorical data is very important when using classification models based on deep learning; however, knowing how to prepare data for regression is as important. Data that contains continuous-like real values, such as temperature, prices, weight, speed, and others, is suitable for regression; that is, if we have a dataset with columns of different types of values, and one of those is real-valued data, we could perform regression on that column. This implies that we could use all the rest of the dataset to predict the values on that column. This is known as univariate regression, or regression on one variable.
Most machine learning methodologies work better if the data for regression is normalized. By that, we mean that the data will have special statistical properties that will make calculations more stable. This is critical for many deep learning algorithms that suffer from vanishing or exploding gradients (Hanin, B....
