Binary-Class Model for Loan Default Dataset Using Scikit-learn

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

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

INTRODUCTION: The dataset owner leveraged another source from Kaggle to create a dataset for predicting loan default. The dataset contains the past data on the loan borrowers, and we would develop a machine learning model to classify whether any new borrower is likely to default.

ANALYSIS: The average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 0.9932 using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 1.0. When we processed the test dataset with the final model, the model also achieved a ROC/AUC score of 1.0.

CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Loan Default Dataset by M Yasser H

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference:

The HTML formatted report can be found here on GitHub.