A Decision Tree Classifier Based Ensemble Approach to Credit Score Classification

The process of classifying credit scores holds a crucial role in evaluating an individual's creditworthiness, influencing significant financial choices. This study is driven by the dynamic nature of credit scores and the financial sector's need for precise, real-time credit evaluations. This research introduces an ensemble-based method for credit score classification, utilizing a blend of diverse machine learning algorithms to improve accuracy and resilience. The ensemble approach capitalizes on each base classifier's strengths, mitigating biases, reducing overfitting, and enhancing overall classification accuracy. A comparison between the proposed model and existing frameworks demonstrates its competitive edge, surpassing many counterparts with an accuracy of approximately 92.25%. However, the study acknowledges the potential for further enhancement and validation across various datasets. The ensemble-based framework offers a promising avenue to heighten credit score classification accuracy, thereby contributing to informed financial decision-making and reinforcing credit ecosystem stability. Future endeavors involve expanding the model to include more datasets and refining data preprocessing techniques to achieve even more precise predictions.