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@article{169610,
        author = {Panthangi Praharshitha and Vurimi Venkata Krishna Vamsi and Pusuluri Pujitha and Thota Mohan Koteswar and Vinoj J},
        title = {Credit Risk Analysis Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1551-1557},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169610},
        abstract = {Credit risk assessment is a critical task for financial institutions when determining applicants’ eligibility for credit products, such as credit cards. This study explores the use of machine learning techniques to predict credit card approval outcomes based on applicants’ demographic and financial information. The analysis uses the ”Credit Card Approval Prediction” dataset from Kaggle, comparing two machine learning models: Logistic Regression and Random Forest Classifier. After performing necessary data preprocessing, including handling missing values, encoding categorical variables, and addressing class im- balance through SMOTE, the models are evaluated using metrics like accuracy, precision, recall, and ROC-AUC. The results show that Random Forest Classifier outperforms Logistic Regression in prediction accuracy, demonstrating its potential to enhance decision-making in credit risk management.},
        keywords = {Credit Risk Assessment, Credit Card Approval, Machine Learning, Logistic Regression, Random Forest Classifier, SMOTE, Data Preprocessing, Class Imbalance, Model Evaluation, Financial Risk},
        month = {November},
        }