Abstract
Data mining and machine learning models are frequently used today to generate insights in churn analysis. Through churn analysis, businesses can make inferences before their customers leave the company or stop using their products, and can increase both profits and customer satisfaction by reducing customer churn. There are many ways to perform these analyses. Rule-based models can be developed and predictions can be made with various machine learning models. In this article, machine learning models were built and analyzed on a data set consisting of 7043 observations and 57 variables, which is publicly available on kaggle.com. As a result of this analysis using customers' data, it was predicted which customers will continue to be customers of the telecom company and which customers will leave the company. It is discussed which features are important on the churn situation. Light GBM, XGBoost, CatBoost, and Gradient Boosting methods are used as machine learning models and the performances between these boosting methods are evaluated. The data set is balanced by applying resampling techniques. Model performance was assessed based on accuracy, F1-Score, and sensitivity metrics. When metrics accuracy and F1-Score were evaluated, no significant difference was found in the model performances. However, when metric sensitivity was assessed, the best performance was achieved by the Extreme Gradient Boost (XGBoost) model with a ratio of 0.949.