Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction

•Application of ML models to predict electric vehicle charging station choice behavior.•Compared the performance of XGBoost, Random Forest, Navie Bayes, and Logistic Regression.•Sensitivity analysis is conducted to uncover the significant predictor variables.•Employed the SHAP approach to tackle the non-interpretability issue of the ML models.