<![CDATA[This study aims to analyze and compare the performance of the Random Forest and Gradient Boosting algorithms in classifying credit card default risk using the Default of Credit Card Clients dataset. The dataset consists of 30,000 entries with 24 financial and demographic variables representing customers’ payment behavior over the past six months. The research procedure includes data acquisition and exploration, preprocessing through feature standardization, stratified data splitting, model construction, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that both algorithms successfully capture complex financial patterns; however, Gradient Boosting demonstrates superior performance, particularly in recall and F1-score, highlighting its better sensitivity to default cases. The feature importance analysis confirms that payment history variables, especially PAY_0, play a major role in influencing model predictions. Overall, this study recommends the use of boosting-based models for credit risk prediction, particularly when dealing with imbalanced datasets, due to their ability to learn minority patterns and iteratively reduce prediction errors.]]>
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