Customer Churn Prediction Using Apriori Algorithm and Ensemble Learning

Customer churn poses a formidable challenge within the Telecom industry, as it can result in significant revenue losses. In this research, we conducted an extensive study aimed at developing a viable customer churn prediction method. Our method utilizes the Apriori algorithm's strength to identify the key causes of customer churn. In the pursuit of this goal, we utilized multiple machine learning predictive models. All of which were developed from the insights gleaned from the Apriori algorithm's feature extraction for churning customers. This extensive analysis encompassed a spectrum of machine learning techniques that include Logistic Regression, Naive Bayes, Support Vector Machines, Random Forests, and Decision Trees. Furthermore, we utilized an ensemble learning approach to enhance the predictive accuracy of our models. We also used a voting classifier refined with the best features within our dataset. The voting classifier yielded an accuracy rate of 81.56%, underscoring the effectiveness of our approach in addressing the critical issue of customer churn in the Telecom industry. © 2023 IEEE.