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The revenue from the loans directly accounts for the majority of the bank's profit. Additionally, one of the key characteristics of the banking industry is the credit danger.The vaticination of credit defaulters is one of the delicate tasks for any bank, but by vaticinating the loan defaulters, the banks surely may reduce their loss by reducing its non-profit means so that recovery of approved loans can take place without any loss and it can play as the contributing parameter of the bank statement. This makes the study of this loan eligibility vaticination important. Machine learning ways are veritably pivotal and useful in the vaticination of these types of data. In order to save a lot of money and bank sweats, we attempt to lessen the threat element that drives people to choose the secure person in this paper. This is accomplished by mining the Big Data belonging to the previously loan issued individuals, and based on this data, machine learning models were taught to produce the most accurate results. This paper's main goal is to determine which machine algorithm performs best at predicting whether or not a person is qualified for a loan.