Binary Classification Model for Credit Card Default Using Python and AutoKeras

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: After a series of modeling trials, the AutoKeras system processed the validation dataset with a maximum accuracy score of 81.98%. When we applied the best AutoKeras model to the previously unseen test dataset, we obtained an accuracy score of 81.25%.

CONCLUSION: In this iteration, the best TensorFlow model generated by AutoKeras appeared to be suitable for modeling this dataset. We should consider experimenting with AutoKeras 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.