A Machine Learning Approach for Customer Churn Prediction

In the dynamic and fiercely competitive landscape of the telecommunications industry, customer churn prediction poses a significant challenge due to the continuously evolving customer behavior and churn factors. Traditional predictive models, while effective to some extent, often fall short in addressing these changing parameters, leading to suboptimal accuracy in predicting customer churn. Addressing this limitation, this paper presents an innovative approach named the Evolving Ensemble Predictor (EEP) for churn analysis. This approach treats churn prediction as an evolving, rather than a static problem. EEP integrates machine learning models - neural networks, random forest, XGBoost, and KNN - known for their adaptability and proficiency in dealing with drifting variables over time. The strength of this method lies in its ensemble learning capability, employing a weighted average technique. This allows for harnessing the unique strengths and diversity of each incorporated model, thus resulting in a robust and adaptive predictive system. The performance of the EEP, in terms of accuracy and computational efficiency, is rigorously evaluated using the Orange Telecom's Churn Dataset available on Kaggle. The results are compared to existing state-of-the-art models, revealing substantial improvements in churn prediction accuracy and computational cost with the EEP approach. In essence, our EEP model offers a significant leap forward in predicting customer churn in a dynamic environment. This research lays a concrete foundation for service providers in the subscription-based industry to devise effective customer retention strategies and maintain a competitive edge in the relentlessly evolving market.