Binary Classification Model for Credit Card Default Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Credit Card Default dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: This dataset contains information on default payments, demographic factors, credit data, payment history, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.

ANALYSIS: The baseline performance of the XGBoost algorithm achieved an accuracy benchmark of 81.44%. After a series of tuning trials, the XGBoost model processed the training dataset with an accuracy score of 82.20%. When we applied the XGBoost algorithm to the previously unseen test dataset, we obtained an accuracy score of 81.81%.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset. We should consider using the algorithm for further modeling.

Dataset Used: Default of Credit Card Clients Dataset

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

The HTML formatted report can be found here on GitHub.