Credit Score Classification Using Machine Learning

Credit score classification plays a significant role in the financial sector, influencing decisions regarding loans, credit cards, and other financial products. Traditionally, credit scores have been calculated based on financial history, and customers are classified into categories such as “Good,” “Standard,” and “Poor.” However, with advancements in machine learning, there is a potential to improve the prediction accuracy and reliability of these classifications. This paper aims to explore the use of machine learning algorithms to classify customers into these credit score categories using a labelled dataset of credit card customers. We apply various machine learning models, such as Decision Trees, Random Forests, and Neural Networks, to predict credit scores and assess their performance based on key evaluation metrics. Our results suggest that machine learning approaches can significantly enhance the accuracy of credit score classification, offering a promising solution for financial institutions looking to automate and improve their lending processes. We observed that Random Forests outperformed other models in terms of accuracy and recall, achieving an F1 score of X%.