Binary Classification Model for LendingClub Loan Data Using Python and Scikit-Learn

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

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

INTRODUCTION: The Kaggle dataset owner derived this dataset from the publicly available data of Lending Club connects people who need money (borrowers) with people who have money (investors). An investor naturally would want to invest in people who showed a profile of having a high probability of paying back the loan. The dataset uses the lending data from 2007 to 2010, and we will try to predict whether the borrower paid back their loan in full.

ANALYSIS: The average performance of the machine learning algorithms achieved a ROC-AUC benchmark of 0.7824 using the training dataset. We selected Random Forest and Extra Trees to perform the tuning exercises. After a series of tuning trials, the refined Extra Trees model processed the training dataset with a final ROC-AUC score of 0.8914. When we processed the test dataset with the final model, the model achieved a ROC-AUC score of 0.6064.

CONCLUSION: In this iteration, the k-Nearest Neighbors model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle LendingClub Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.