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Published Paper Details:
Paper Title
Customer Credit Default Prediction
Authors
Mallesham Devasane
Keywords
Credit Default, Prediction, Machine Learning Models, Random Forest, Logic Regression, XGBoost, Decision Tree
Abstract
This document presents a data-driven approach to identify customers who may fail to repay their credit obligations. The study used structured financial records that included behavioral and personal characteristics to build and evaluate prediction tools.
After cleaning the data for missing values, converting categories, and scaling numeric fields, we scaled it for pattern and distribution analyses. We trained and validated the XGBoost, Random Forest, Decision Tree, and Logistic Regression algorithms.
To determine the performance of each model, we used the F1 Score, Precision Score, ROC AUC and Recall Score. Among all the tested algorithms, the predictions of the Random Forest algorithm were very reliable owing to its accurate interpretability and prediction.
IJCRT's Publication Details
Unique Identification Number - IJCRT25A6097
Paper ID - 290062
Page Number(s) - j415-j424
Pubished in - Volume 13 | Issue 6 | June 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Mallesham Devasane, "Customer Credit Default Prediction", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.j415-j424, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A6097.pdf
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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