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Credit ratings play a crucial role in financial risk assessment, influencing borrowing costs, investment decisions, and overall market stability. As corporate financial data becomes more intricate, machine learning (ML) emerges as a promising tool to enhance traditional credit evaluation methods. This thesis investigates the predictive performance of six supervised ML algorithms—Support Vector Mac
