Enhancing costumer churn prediction with stacking ensemble and stratified k-fold

Abstract

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.