Version 1
: Received: 21 August 2023 / Approved: 21 August 2023 / Online: 21 August 2023 (13:12:21 CEST)
Version 2
: Received: 24 September 2023 / Approved: 25 September 2023 / Online: 26 September 2023 (05:17:49 CEST)
Version 3
: Received: 7 November 2023 / Approved: 8 November 2023 / Online: 8 November 2023 (10:22:33 CET)
Version 4
: Received: 16 November 2023 / Approved: 17 November 2023 / Online: 17 November 2023 (14:15:58 CET)
Imani, M.; Arabnia, H.R. Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis. Technologies2023, 11, 167.
Imani, M.; Arabnia, H.R. Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis. Technologies 2023, 11, 167.
Imani, M.; Arabnia, H.R. Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis. Technologies2023, 11, 167.
Imani, M.; Arabnia, H.R. Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis. Technologies 2023, 11, 167.
Abstract
This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics such as Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). Regarding the F1-Score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
17 November 2023
Commenter:
Mehdi Imani
Commenter's Conflict of Interests:
Author
Comment:
Some changes we have implemented:
Abstract Revision: The abstract has been thoroughly rewritten to improve its readability and flow. This revision aims to provide a clearer and more concise overview of the study, making it more accessible to readers.
Clarification in Introduction: We have carefully rephrased several sentences in the introduction that were previously unclear. These modifications are intended to present the research context and objectives more coherently, ensuring that readers can easily grasp the study's premise.
Addition of Table 1: To further aid in readability and comprehension, we have included Table 1 in the manuscript. This table is designed to succinctly present important acronyms.
Commenter: Mehdi Imani
Commenter's Conflict of Interests: Author
Abstract Revision: The abstract has been thoroughly rewritten to improve its readability and flow. This revision aims to provide a clearer and more concise overview of the study, making it more accessible to readers.
Clarification in Introduction: We have carefully rephrased several sentences in the introduction that were previously unclear. These modifications are intended to present the research context and objectives more coherently, ensuring that readers can easily grasp the study's premise.
Addition of Table 1: To further aid in readability and comprehension, we have included Table 1 in the manuscript. This table is designed to succinctly present important acronyms.