Predictive Analytics for Default of Credit Card Clients

Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepared data sample. We experimentally evaluated the classification models within metrics like accuracy, precision, recall, ROC, and AUC. The results show a very similar performance of the selected algorithms on this dataset. The Gradient boosting (0.7828) achieved the best performance within AUC, but the best precision for target class 1 reached the Bagging algorithm (0.72). The simple data processing brought only minimal improvements in individual metrics. Our results are comparable to the mentioned studies instead of MCC metrics that resulted in better value (0.4111) achieved by the Gradient Boosting model.