Ensemble Classification using Balanced Data to Predict Customer Churn: A Case Study on the Telecom Industry

This research examines the factors affecting customer churn in the telecom industry. In this regard, we use data mining classification methods, such as neural network, K-nearest neighbor, support vector machine, logistic regression, decision tree, and random forest. The results are analyzed using various criteria such as accuracy, precision, recall, F1-score, and ROC curve. After determining the successful classifiers, we combine them with the AdaBoost and XGBoost methods.