Comparative Analysis of Machine Learning Models for Credit Scoring: A Case Study on the South German Credit Dataset

Abstract

In this research, proposed a novel hybrid machine learning approach that combines the strengths of Random Forest, Multi-Layer Perceptron (MLP), and LightGBM algorithms for classification tasks. This research work focuses on evaluating the performance of this hybrid model using the South German Credit dataset obtained from Kaggle, comprising bank client data, client last contact information, and labels. With 45,211 records and 16 attributes, this dataset provides a suitable environment for assessing the effectiveness of our proposed approach. Employ various evaluation metrics including accuracy, sensitivity and specificity and Receiver Operating Characteristic (ROC) to comprehensively analyze the model's performance. Through experiments, aim to demonstrate the efficacy of the hybrid approach in accurately classifying instances and providing insights into its potential applications in real-world scenarios