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@article{201293,
        author = {YELAGARI VIGHNESH GOUD VIGHNESH GOUD and V. SHASHIDHAR and V. PRABHU KUMAR and U. SIVA PRASAD and Mrs.D.BHAVANA M.E},
        title = {Loan Approval Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3199-3208},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201293},
        abstract = {The loan approval prediction system using machine learning aims to automate and enhance the decision-making process in financial institutions. Traditional loan approval methods are often time-consuming, prone to human errors, and may involve subjective judgment. To overcome these challenges, the proposed system utilizes machine learning algorithms to analyze applicant data and predict loan approval outcomes. By leveraging historical data, the system identifies patterns and relationships between various features such as income, credit history, and employment status. This enables faster and more accurate decision-making while reducing manual effort. The proposed approach involves several key stages, including data collection, preprocessing, feature extraction, and model training. Data preprocessing techniques such as handling missing values, encoding categorical variables, and normalization ensure that the dataset is suitable for analysis. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, are used to build predictive models. These models are evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that the system achieves high accuracy and effectively classifies loan applications as approved or rejected. The developed system provides significant benefits to financial institutions by improving efficiency, reducing operational costs, and minimizing the risk of loan defaults. It also enhances customer experience by delivering quick and reliable decisions. The integration of machine learning ensures consistent and unbiased evaluation of applicants. Furthermore, the system can be deployed as a web-based application for real-time predictions. Overall, the project highlights the importance of intelligent and data-driven solutions in modernizing loan approval processes and supporting better financial decision-making.},
        keywords = {Loan Approval Prediction, Machine Learning, Classification, Data Preprocessing, Logistic Regression, Decision Tree, Random Forest, Financial Risk Analysis, Credit Scoring, Predictive Modeling},
        month = {May},
        }