Credit default decision tree

<p>This workflow reads in credit data, prepares it, and builds a <strong>Decision Tree</strong> model to predict whether a debtor will default. The data is first imported and formatted, then split into <strong>training</strong> and <strong>test</strong> sets. The training set is used to create a decision tree model, which is then applied to the test set to generate predictions. The accuracy of these predictions is evaluated, and the decision tree is also converted into a set of <strong>human-readable rules</strong> for easier interpretation.</p>