Binary Classification Deep Learning Model for Credit Card Default Using Keras

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 are trying to predict one of the two possible outcomes.

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

ANALYSIS: The baseline performance of the model achieved an average accuracy score of 81.11%. After tuning the hyperparameters, the best model processed the training dataset with an accuracy of 82.00%. Furthermore, the final model processed the test dataset with an accuracy of 81.41%, which was consistent with the result from model training.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Default of Credit Card Clients Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

The HTML formatted report can be found here on GitHub.