This project develops a machine learning model to predict loan repayment likelihood for better risk management. Using a labeled dataset, it includes data preprocessing (outlier removal, format standardization, missing value imputation) and applies various classifiers to evaluate their effectiveness in predicting repayment probability.
This project is designed to enhance risk management and analysis in the financial sector by employing machine learning (ML) techniques to predict and classify loan repayment behavior. Utilizing a comprehensive labeled dataset, the project applies an array of preprocessing techniques to ensure data integrity and suitability for ML models. This includes the removal of outliers, reformatting data for model compatibility, and addressing missing values.
Following preprocessing, a diverse set of classification algorithms is implemented to evaluate their effectiveness in predicting loan repayment outcomes. The models employed include Logistic Regression, Linear Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, and XGBoost. Each model is assessed based on its performance in classifying whether a borrower is likely to repay the loan or default.
The objective of this project is to provide a robust analytical tool that assists financial institutions in risk management by accurately forecasting loan repayment behaviors. The insights gained from this analysis are intended to inform decision-making processes and improve the overall efficiency of loan approval and monitoring systems.