Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis

Mehdi Imani · Hamid Reza Arabnia

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Customer churn and segmentation Machine Learning and Data Classification Imbalanced Data Classification Techniques

Abstract

In this paper, a variety of machine learning techniques, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and three gradient boosting techniques (XGBoost, LightGBM, and CatBoost), were employed to predict customer churn in the telecommunications industry using a publicly available dataset. To address the issue of imbalanced data, various data sampling techniques, such as SMOTE, the combination of SMOTE with Tomek Links, and the combination of SMOTE with Edited Nearest Neighbors, were implemented. Additionally, hyperparameter tuning was utilized to optimize the performance of the machine learning models. The models were evaluated and compared using commonly used metrics, including Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). The results revealed that the performance of the models was enhanced by the application of hyperparameter tuning and the combined data sampling methods on the training data. Overall, LightGBM demonstrated superior performance compared to the other machine learning techniques examined. The findings indicate that LightGBM exhibited a superior performance both prior to and following the application of these techniques.

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References

2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2019Customer churn prediction in telecom using machine learning in big data platform 2016Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2021Customer churn prediction system: a machine learning approach 2018Customer churn prediction in telecommunication industry using data certainty 2010Data Mining Techniques in CRM: Inside Customer Segmentation

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