Loan Approval Prediction Using Machine Learning

Loan Prediction is an essential problem in the banks and finance industries.Accurately prediction whether a loans will be approved or rejected can help financial institutions to manage risk, reduce defaults, and increase profitability.Machine learning algorithms have emerged as a powerful tool for loan prediction, allowing banks to analyze large datasets and make more informed lending decisions.We explore the various machines learning algorithm that has been applied to loan prediction, includes decisions tree, random forest, XGBoost, and neural network.We also discuss the key features used in loan prediction, such as loan amount, income, credit history, and loan term.Additionally, we compares the performance of several machine learning algorithm by using metric like accuracy, precisions, recalls and last F1 scores.This result shows that machine learning algorithms can significantly improve the accuracy and efficiency of loan prediction, providing valuable insights into the risk and profitability of loan portfolios.We also highlight the challenges and limitations in prediction of loans by using machines learning and identify upcoming researches directions in this fields.This paper provides a valuable resources for researcher and practitioners interest in loan prediction using machines learning.