Binary-Class Loan Approval Classification Employing Decision Tree

Abstract

Banks' profitability is highly dependent on loans, which constitute a substantial amount of their income. However, the task of precisely selecting real persons who would repay their loans becomes challenging due to the high volume of loan applicants. The bank's revenue and profitability are directly affected by the choice to accept or reject a loan application. The manual review of loan applications is susceptible to misconceptions and inaccuracies, resulting in the approval of applicants who may not truly be creditworthy. The aim of this study is to develop a loan prediction system employing machine learning approaches to address the issue. The system will autonomously look over and choose suitable applicants for loans, thus reducing the need on manual processing. The suggested framework uses the Decision Tree method, an algorithm for machine learning that can make judgments based on input data. The Decision Tree algorithm analyzes past loan data, considering into account numerous factors such as income, credit score, employment history, and loan amount. It utilizes this data to generate a prognostic model that evaluates the probability of loan reimbursement. By utilizing historical loan data and information from past loan applicants, the model has the ability to forecast whether a loan application should be approved or denied. Using such a system offers benefits to both employees at banks and those applying for loans. Automating the selection process greatly decreases the time needed for loan approval, allowing applicants to access funds more quickly. In addition, the technique enhances the accuracy of applicant screening, diminishing the likelihood of providing loans to those who have a higher probability of defaulting. The project efforts to make use of machine learning approaches, specifically the Decision Tree classifier, in order to forecast loan outcomes and improve the loan approval process. This automated method helps tackle the difficulties linked to manual processing and enhances the efficiency and precision of selecting loan applicants. In conclusion, this project effectively created a highly precise and efficient Decision Tree model that can greatly enhance the loan approval process for banks. It achieves this by automating predictions, minimizing manual work, and delivering dependable loan approval or rejection outcomes that align with the requirements of the banking sector