Implementation of classical and quantum machine learning algorithms for a credit scoring scenario
Applying machine learning and quantum machine learning for credit scoring challenges requires the development of a prediction model that can properly determine an individual’s or company’s creditworthiness. Typically, this procedure, as shown in the steps described previously, includes data collection, data enrichment, data preparation, feature engineering, feature selection, model selection, model training, model evaluation, and subsequently, deployment. In this section, we will cover most of the previous concepts and procedures, assuming that the data is already encoded to numerical variables and the feature has been selected.
Data preparation
First, the data needs to be loaded. This data will come in one of the more well-known formats in the industry, which is CSV. The information that will load into the notebook, as previously detailed, is in a classical format...
