Loan-Approval-Prediction
A Random Forest model is employed in a bank loan approval system to assess applicant eligibility. By utilizing a collection of decision trees, it analyzes various factors such as credit history, income, and debt-to-income ratio to predict loan approval likelihood. The algorithm’s ensemble nature enhances accuracy by aggregating multiple tree predictions, offering a robust evaluation method. Through this approach, the system efficiently determines loan eligibility, contributing to informed and reliable lending decisions.