On Kaggle, there is a dataset called "Credit EDA Dataset" that includes details on credit card holders' credit histories, including credit limits, payment histories, balances, and demographic data. Both credit risk prediction and exploratory data analysis (EDA) can be done with it. Regression analysis, decision trees, and neural networks are just a few examples of the statistical and machine learning methods that can be used to analyze the data. It is designed to be used for research and educational purposes and can be helpful for examining customer behavior and spotting trends that can help with credit risk evaluations. Seaborn, Pandas, Matplotlib, and Numpy were the tools used.