Introduction

In the previous chapter, we learned about the building blocks of DNNs and reviewed the characteristics of the three most common architectures. Additionally, we learned how to solve a regression problem using a DNN.

In this chapter, we will use DNNs to solve a classification task, where the objective is to predict an outcome from a series of options.

One field that makes use of such models is banking. This is mainly due to their need to predict future behavior based on demographic data, alongside the main objective of ensuring profitability in the long term. Some of the uses in the banking sector include the evaluation of loan applications, credit card approval, the prediction of stock market prices, and the detection of fraud by analyzing behavior.

This chapter will focus on solving a classification banking problem using a deep artificial neural network (ANN), following all the steps required to arrive at an effective model: data exploration, data preparation...