Improving Credit Score Classification Using Predictive Analysis and Machine Learning Techniques

Rahul M. Koshti; Prof. Shailesh J. Molia; Prof. (Dr) Dhaval J. Varia

doi:10.71097/IJSAT.v16.i2.4759

Improving Credit Score Classification Using Predictive Analysis and Machine Learning Techniques

Author(s)	Rahul M. Koshti, Prof. Shailesh J. Molia, Prof. (Dr) Dhaval J. Varia
Country	India
Abstract	Credit score classification is crucial to financial decision-making, allowing institutions to evaluate creditworthiness and manage risk accurately. Statistical models, on which conventional credit scoring techniques are often based, have difficulty capturing subtle patterns in credit information. This research examines the performance of various ML models such as Decision Trees, Logistic Regression, SVM, Random Forest, LightGBM, Gradient Boosting (GB), AdaBoost, XGBoost and Naive Bayes (NB) on three benchmark datasets: Australian Credit dataset, German Credit dataset, and Kaggle Credit Score Classification dataset. All the models are run through feature selection, hyperparameter tuning to achieve improved prediction accuracy. Experimental findings reveal that Random Forest performs significantly better than all other models with the highest accuracy of 90.58% on the Australian Credit dataset, and XGBoost performed next best but at a slightly lower level than RF model. The above findings are indicative of the power of ensemble learning methods in credit risk assessment and offer a solid model for data-driven financial decision-making and mitigating lending risks for banks.
Keywords	Credit Score Classification, Machine Learning, Credit Risk, Predictive Analysis, Feature Selection, Model Optimization
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-08
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4759
Short DOI	https://doi.org/g9hspr

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Improving Credit Score Classification Using Predictive Analysis and Machine Learning Techniques

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