Loan-Repayment-Prediction

Tools Used: Python,Ms-Excel Algorithms Used -Simple Linear Regression -Logistic Regression -K-Nearest Neighbour -Support Vector Machine -Random Forest -Decision Tree -Artificial Neural Network In the domain of financial lending, accurately predicting loan repayment is a critical task for financial institutions. This project aims to develop a loan repayment system that utilizes machine learning algorithms to predict the risk of loan default and manage repayment processes effectively.
📈 Results Snapshot -Logistic Regression: Accuracy - 77.78%, Precision - 78.77% -K-Nearest Neighbour: Accuracy - 86.67%, Precision - 75.58% -Support Vector Machine: Accuracy - 84.44%, Precision - 85.11% -Random Forest: Accuracy - 84.44%, Precision - 84.49% -Decision Tree: Accuracy - 82.22%, Precision - 80.6% -Artificial Neural Network-71.33%