Comparing machine learning algorithm for predicting loan application for performance enahncement
Authors
Oparaocha, Elizabeth
Issue Date
2024-05
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
Abstract
This study evaluates various machine learning algorithms for predicting bank loan status using the CRISP-DM methodology. The research utilizes a dataset from Kaggle, focusing on algorithms such as Decision Trees, Support Vector Machines, AdaBoost, Random Forests, K-Nearest Neighbours, and Logistic Regression. Models were optimized using GridSearchCV, with recall as the primary metric to highlight the importance of accurately detecting loan repayment patterns.
The findings demonstrate that the Support Vector Machine model was the most effective, achieving a recall score of 0.99and an F1 score of 0.96. Ensemble methods, which combine multiple models, notably improved prediction accuracy while maintaining interpretability. This study identifies the most effective algorithms and provides insights into factors influencing loan decisions, offering practical recommendations for banks to reduce bad loan rates and promote sustainable lending practices through advanced machine learning techniques.