Published Paper Details:

The method of credit scoring involves a set of decision models that help in granting consumers loan and credit [1]. Since it involves many decisive statements, we employ machine learning models to predict the credit score of a consumer. The following chapter will investigate into the details of machine learning or ML model usages, such as the understanding of model outputs, structure modification, and constant investigation of work [2]. These understandings intend to cultivate the prognostic abilities, donating implicitly to the dominion of credit score forecast. Credit assessment has an essential part in the monetary business, leading the lending choices and handling the danger. Usually, this evaluation depends on physical approaches and comparatively humble scoring representations [3]. By bridging research gaps in credit scoring methodologies, specifically, model interpretability, fairness, and adaptability this project makes substantial contributions to advancing the comprehension and application of machine learning in the realm of credit risk assessment. The system's design prioritizes simplicity and transparency, fostering reproducibility and accommodating future expansions in credit scoring research and practical implementations.