Financial institutions assess customers' credit risk and repayment ability based on their credit scores. In this project, I used Python for credit score classification, selecting Random Forest as the model. The classification results are divided into three categories: High, Average, and Low. First, I performed exploratory data analysis (EDA) to understand the distribution of each feature, followed by training the Random Forest model using K-fold cross-validation. Afterward, I evaluated the model's classification performance using metrics such as confusion matrix, accuracy, precision, recall, and F1-score. However, due to the small dataset, the model may face potential issues of insufficient generalization or overfitting.