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@article{189565,
        author = {Dr O.YAMINI and S R AJITHA and Dr G.V.RAMESH BABU},
        title = {Prediction of Loan Process Using an Assortment of Supervised Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7129-7134},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189565},
        abstract = {One of the major challenges faced by banks and other financial institutions is the increasing rate of loan default, which leads to the deterioration of their loan assets into non-performing assets. This adversely affects their capital adequacy and profitability and may force them to merge with other entities or close down. Therefore, it is imperative to assess the risk of loan default of potential borrowers before granting them loans. The goal of this research is to create a predictive model that can estimate the probability of loan default based on a range of variables, including age, education level, number of dependents, income level and source of income. These are a few fundamental standards that can be used to determine who credit is worthy and prevent unfavorable loans. This paper aims to automate the loan approval process using a predictive model with support from various supervised learning algorithms. This can improve service quality and efficiency while reducing the need for human resources, but it will require more computing power.},
        keywords = {Decision making systems, Node Split, Multivariate attributes, Pruning, Feature Selection},
        month = {December},
        }