Loan eligibility prediction plays a critical role in the financial sector, helping banks and lending institutions assess the creditworthiness of applicants before approving loans. Traditional loan approval processes rely heavily on manual assessment and rule-based approaches, which can be time-consuming, inconsistent, and susceptible to human bias. With the rapid advancements in artificial intelligence and machine learning, automated loan eligibility prediction systems have gained significant attention for improving accuracy, efficiency, and fairness in decision-making.This study explores the application of machine learning techniques to predict loan eligibility based on various applicant attributes, including income, credit history, employment status, loan amount, and other financial indicators. A dataset containing historical loan applications is used to train and evaluate multiple machine learning models, including Decision Trees, Random Forest, Support Vector Machines (SVM),K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). Feature selection and preprocessing techniques, such as normalization, handling missing values, and categorical encoding, are employed to improve model performance.By leveraging machine learning in loan eligibility prediction, financial institutions can streamline the approval process, mitigate risks associated with loan defaults, and enhance customer experience by providing quicker and more reliable loan decisions. This research highlights the potential of data-driven approaches in revolutionizing the financial sector and underscores the importance of adopting intelligent predictive models to optimize loan approval workflows.
K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines, Loan prediction
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