Explainable AI-Based Customer Churn Prediction Using Machine Learning and SHAP Analysis

Abstract: Over Customer churn prediction plays a critical role in withstanding the competition in the contemporary business world. This paper presents an Explainable Artificial Intelligence (XAI) framework for predicting customer churn using advanced machine learning algorithms. The framework employed includes XGBoost, LightGBM, Random Forest, and Ensemble Learning to develop an accurate predictive model. The study used customer demographics and behavior data to train the algorithm. Additionally, the study utilized SHAP (SHapley Additive ExPlanations) to interpret the results by illustrating the key factors associated with customer churn. The results indicated that all the predictive models had good Accuracy, F1-score, and ROC-AUC performance metrics. However, the Ensemble Learning model had the highest ROC-AUC of 93.8%. Thus, the study concludes that the proposed solution is an effective approach to predicting customer churn and aids businesses in making the right decisions based on the results.

Keywords: Explainable AI, SHAP, XGBoost, LightGBM, Random Forest, Ensemble Learning, Machine Learning, Data Analytics.