Predicting Credit Card Defaults with Machine Learning

Abstract

Abstract: This research paper focuses on the application of machine learning techniques to predict credit card defaults. The study utilizes a comprehensive dataset comprising diverse features related to credit card usage and payment behavior. By leveraging this dataset, the research aims to develop and evaluate predictive models using two popular machine learning algorithms: Logistic Regression and Naive Bayes classifiers. In addition to the model implementation and evaluation, the research incorporates exploratory data analysis techniques to gain deeper insights into the dataset. Exploratory data analysis involves visualizing and analyzing key patterns, trends, and relationships within the dataset. By combining predictive modeling and exploratory analysis, this research aims to provide a comprehensive understanding of credit card default prediction, thereby assisting financial institutions in making more informed decisions. The implementation of Logistic Regression and Naive Bayes classifiers allows for a comparison of the performance of these two popular algorithms in predicting credit card defaults. Logistic Regression is a widely used algorithm known for its interpretability and robustness, while Naive Bayes is based on probabilistic principles and is known for its simplicity and efficiency. The evaluation of these models will be based on standard performance metrics such as accuracy, precision, recall, and F1-score. Furthermore, the research employs exploratory data analysis techniques to uncover valuable insights within the dataset. Through visualizations and statistical analysis, this analysis aims to identify correlations, trends, and anomalies that may contribute to credit card defaults. Exploratory data analysis can help uncover hidden patterns and provide valuable contextual information, enhancing the understanding of the underlying factors associated with credit card defaults.