IMPLEMENTATION MEAN IMPUTATION AND OUTLIER DETECTION FOR LOAN PREDICTION USING THE RANDOM FOREST ALGORITHM
DOI:
https://doi.org/10.33480/jitk.v10i4.6437Keywords:
Accuracy, loan prediction, pre-processing, AdaBoost Classifier, Heart Failure, Machine Learning, Random Forest, ClassificationAbstract
Loans and credit are among the most in-demand banking products, making accurate loan prediction systems essential for minimizing bank credit risks and boosting profitability. This study proposed a loan prediction model using the Random Forest algorithm, with mean imputation and 3 outlier detection (Boxplot, Z-score, and Interquartile Range (IQR)) as data pre-processing methods. Using Lending Club loan data from 2014-2021 (466,285 records, split 70/30 for training/testing), model performance was assessed using accuracy, recall, and F1 Score. The proposed approach achieved a 95% prediction accuracy, outperforming previous models at 83%. The best results were obtained using mean imputation with IQR-based outlier detection. However, the determination of the mean imputation mean can be a limitation of this study. This highlights the importance of thorough pre-processing in enhancing prediction accuracy. The study underscores the role of machine learning and financial technology (fintech) in informing credit decisions and support incorporating imputation and outlier handling as standard steps in financial modeling pipeline
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