COMPREHENSIVE PERFORMANCE ANALYSIS OF MACHINE LEARNING BASED ALGORITHMS FOR CREDIT DEFAULT PREDICTION USING LENDING CLUB DATASET
DOI:
https://doi.org/10.22159/ijet.2025v13.57906Keywords:
Credit default prediction, Machine learning, XGBoost, Lending club dataset, Credit risk assessment, Predictive analyticsAbstract
Objective: To develop and evaluate a machine learning–based framework for accurate prediction of credit default using the Lending Club dataset.
Methods: The study applies extensive data preprocessing including missing value treatment, outlier removal using Z-score, one-hot encoding of categorical variables, Min–Max normalization, and class balancing using SMOTE. Several models were evaluated, with Extreme Gradient Boosting (XGBoost) used as the primary classifier. Data were split into 80% training and 20% testing. Performance was measured using accuracy, precision, recall, and AUC.
Results: XGBoost achieved the best performance with accuracy of 91.56%, precision of 94.78%, recall of 93.30%, and AUC of 97.07, outperforming decision tree, logistic regression, artificial neural networks, gradient boosting, and convolutional neural networks.
Conclusion: The proposed XGBoost-based framework demonstrates strong potential for real-world credit risk assessment by effectively handling large and imbalanced datasets and improving early identification of default risk.
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