Customer churn prediction in an internet service provider

Customer retention is regarded as one of the most concerns in any company, since they provide the fundamental source of revenue for business. Losing customers not only loses the profit, but also may put a whole business in danger. In order to increase customer base, businesses need to improve both acquisition and retention of its customers. Therefore, customer churn prediction is becoming the top concerns that many companies devoted their time and resources to address it. This paper presents the customer churn prediction on an extremely imbalanced data in an Internet Service Provider company to identify the users at the risk of leaving the services. It consists of feature engineering and predictive modeling. In the feature engineering, the most essential features are selected from a large number of created candidates. In the predictive modeling, the imbalance between the number of churners and non-churners was reduced using SMOTE oversampling technique before implementing several models such as AdaBoost, Extra Trees, KNN, Neural Network and XGBoost. Comparing between these models in term of precision and recall, the XGBoost model gives the highest performance. Using the dataset with 98% non-churners and 2% churners, precision and recall of the model are 45.71% and 42.06%, respectively.