Loan Repayment Predictive Model Using Ensemble Algorithm

The accurate prediction of loan repayment ability is paramount in any financial industry to minimize risks and enhance decision making. This project centers on the development of a loan repayment predictive model using ensemble algorithm. The performance of the algorithm is evaluated through the following metrics: f1 score, accuracy, and precision. Five classifiers including Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, and SVC were used as base classifiers for the ensemble. Ensemble-based optimization (using RSCV) is employed to get the best combination of hyperparameters. This technique performs a randomized search over specified hyperparameter values, using cross-validated performance to find the best combination to create models that make up the ensemble model. Preliminary results indicate promising performance improvements when compared to single model. Future research in this domain could further refine the model and expand its applications in the ever-evolving financial sector.