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@article{179957,
        author = {Uday R and Lokesh GS and Darshan J and Punith SB},
        title = {Loan Eligibility and Approval Prediction Using Machine  Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8585-8587},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179957},
        abstract = {This paper presents a real-time loan eligibility 
prediction system using machine learning techniques. 
The system leverages historical financial data to assess 
applicant profiles and predict loan approval outcomes. 
Classification algorithms such as Support Vector 
Machines (SVM), Logistic Regression, and Random 
Forest are employed to identify patterns in applicant data 
and deliver accurate predictions. The proposed system 
automates the decision-making process, reduces human 
bias, and increases processing speed and consistency. A 
web-based interface facilitates seamless data input and 
displays real-time results, enabling practical use in 
financial institutions. Experimental results demonstrate 
the system's effectiveness, with high prediction.},
        keywords = {Loan Eligibility, Machine Learning, SVM,  Logistic Regression, Random Forest, Credit Prediction,  Real-Time Decision, Financial Automation.},
        month = {May},
        }