Preparing the data for the Keras library
In Chapter 1, Neural Networks and Gradient-Based Optimization, we saw that neural networks would only take numbers as inputs. The issue for us in our dataset is that not all of the information in our table is numbers, some of it is presented as characters.
Therefore, in this section, we're going to work on preparing the data for Keras so that we can meaningfully work with it.
Before we start, let's look at the three types of data, Nominal, Ordinal, and Numerical:
Nominal data: This comes in discrete categories that cannot be ordered. In our case, the type of transfer is a nominal variable. There are four discrete types, but it does not make sense to put them in any order. For instance, TRANSFER cannot be more than CASH_OUT, so instead, they are just separate categories.
Ordinal data: This also comes in discrete categories, but unlike nominal data, it can be ordered. For example, if coffee comes in large, medium, and small sizes, those are distinct...
