Ensemble Based Efficient Churn Prediction Model for Telecom

Predicting churners in telecom is an important application area of pattern recognition that helps in responding appropriately for retaining customers and saving the revenue loss a corporation suffers. The aim of the churn predictor is to learn the pattern of churners and thus differentiate between churners and non-churners. Handling the large dimensionality and selecting discriminative features are challenging aspects of telecom churn prediction that hinder the performance of predictors. In this study, we propose a churn prediction approach that exploits the discriminative feature selection capabilities of minimum redundancy and maximum relevance in the first step, leading to enhanced feature-label association and reduced feature set. The diverse ensemble of different base classifiers is then applied as a predictor in a second step. Final predictions are computed based on majority voting Random Forest, Rotation Forest and KNN, that ultimately leads to predicting churners from telecom datasets with higher accuracy. Simulation results are evaluated using sensitivity, specificity, area under the curve (AUC) and Q-statistic based measures on standard telecom datasets. The results indicate that our proposed approach efficiently models the challenging problem of telecom churn prediction, by effectively handling the large dimensionality and extending useful features to a diverse, majority voting based ensemble.