Monetary Loan Eligibility Prediction using Logistic Regression Algorithm

In the banking sector, predicting customer loan eligibility is a crucial task. This paper presents a study of using machine learning algorithms to predict loan eligibility of customers based on credit history, income, and others. The algorithms used in the study are Logistic Regression. Using machine learning algorithms the results are demonstrated the potential of in the banking sector to improve loan decision-making processes and increase efficiency. The study utilized a dataset containing information on various loan applications and their respective approvals. The dataset was pre-processed and various machine learning algorithms were applied to the data to determine the best model for predicting loan approval. The findings of this study can be useful for financial institutions in making informed decisions about loan approvals, reducing the risk of default and improving the overall loan approval process. The model is trained using a dataset containing various demographic and financial parameters of loan applicants. The Logistic regression algorithm is utilized to build a predictive model that determines the eligibility of a loan applicant based on the input features. The system aims to modernize the loan approval process by using predictive algorithms to analyze and evaluate loan applications. The study compares different machine learning algorithms, including logistic regression and support vector machines, and evaluates their performance in terms of accuracy and efficiency. The findings also suggest that the use of machine learning can help banks streamline their loan approval processes.