As a final preprocessing step, we should also scale our features before passing them to the neural network. Recall from the previous chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons, that scaling ensures that all features have a uniform range of scale. This ensures that features with a greater scale (for example, year has a scale of > 2000) does not dominate features with a smaller scale (for example, passenger count has a scale between 1 to 6).
Before we scale the features in the DataFrame, it's a good idea to keep a copy of the prescaled DataFrame. The values of the features will be transformed after scaling (for example, year 2010 may be transformed to a value such as -0.134 after scaling), which can make it difficult for us to interpret the values. By keeping a copy of the prescaled DataFrame, we can easily reference the original...
