Customer Churn is now becoming a significant problem in the banking sector. It is necessary to seek solutions to predict the rate of customer churn in banks
however, the dataset for customer churn prediction in banks is imbalanced. In this paper, Random Forest (RF) based on two popular resampling techniques, named SMOTE and ADASYN, are used to obtain a banking customer churn prediction model. A wide range of metrics, including Accuracy, Recall, Precision, Specificity, F1 score, Mathews correlation coefficient, and ROC-AUC, are used to comprehensively evaluate the prediction model. Through the experimental results, the values of Accuracy and ROC-AUC of the RF model based on SMOTE and ADASYN indicate positive results. Moreover, this paper also shows feature importance in the dataset based on the RF algorithm.