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Identifying and predicting default borrowers in P2P lending platform: A machine learning approach

This study aims to compare the Random Forest (RF) ensemble learning method with other machine learning methods to predict the default borrowers. The data was retrieved from LendingClub dataset, and recursive feature elimination (RFE) was performed to obtain ten best features for the proposed model. To compare the data acquired in this investigation, accuracy, recall, precision, and F1-score, as well as the ROC curve, were used. The comparison of different models determines which model is best for the P2P platform.