Data Analysis of Loan Defaulters Prediction Model Using Machine Learning Techniques
DOI:
https://doi.org/10.17010/ijcs/2024/v9/i4/174567Keywords:
AOC_ROC
, Machine Learning, Precision, Recall, SMOTE, Prediction model, Recommendation.Paper Submission Date
, July 5, 2024, Paper sent back for Revision, July 15, Paper Acceptance Date, July 17, Paper Published Online, August 5, 2024.Abstract
In recent times, the defaulters of loans have become a serious concern for the banking sector. To overcome this problem, the proposed prediction model has been recommended to predict the defaulters using different Machine Learning algorithms. This paper builds a loan defaulters prediction model based on real-world user loan data. The SMOTE method is adopted to cope with the problem of imbalance class in the dataset, and then a series of Machine Learning algorithms is used to find the best model; as a result, the Naive Bayes model performed better compared to other models based on the output of Recall, Precision, and AOC_ROC accuracy.
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