In this project, we conducted extensive data cleaning and analysis on a credit score dataset using clustering, regression, classification, and visualization techniques. Through this analysis, we discovered three interesting patterns in the data. We then trained three different models, namely Decision Tree, Random Forest, and K-NN classification, to predict the credit score. We used AUC score as the metric to evaluate and compare the performance of these models. Overall, our study provides insights into the credit score dataset and the effectiveness of different classification models in predicting credit scores.