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Bhushan, Shreyas Bhargav (2024) Enhancing Customer Churn Prediction in the Telecom Sector Using Advance Machine Learning Techniques and Explainable AI. Masters thesis, Dublin, National College of Ireland.
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Abstract
In telecommunications industry, customer churn is one of biggest issues that is faced by the operators, it is phenomenon where the customers stop using their services or switch to other operators. Telecommunications companies profitability can be heavily impacted by high churn rates, as acquiring new customers is often easy when compared to keeping existing customers. The study proposes a robust and explainable machine learning model to predict and control the customer churn. The proposed framework integrates heterogenous multi-stacking of ensemble technique, combining base models like random Forest, XGBoost, k-Nearest Neighbors (KNN) with logistic regression as meta model. To select the significant features we have applied Recursive Feature Elimination (RFE), and Synthetic Minority Oversampling Technique (SMOTE) was implemented to handle the imbalance of the class. Stratified k-fold was applied to cross validate the performance of the models. The multi-stacked model outperformed all the base models with an accuracy of 81%, while maintaining balance between recall and precision. The evaluation metrics like Accuracy, Precision, Recall, F1-score, ROCAUC score and confusion matrix was used to validate the efficiency of the model. To address the “black box” nature of the ensemble model, the Explainable AI technique called as SHapley Additive exPlanations (SHAP) was used to improve the interpretability of the models, the technique provided insights for both global and local important features. SHAP helped to identify the significant features influencing the churn like contract type, tenure, and monthly charges. These insights help to gain the trust of the stakeholders and design targeted retention strategies.
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