Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are requ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/4742 |
| _version_ | 1797533170305859584 |
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| author | Tianpei Xu Ying Ma Kangchul Kim |
| author_facet | Tianpei Xu Ying Ma Kangchul Kim |
| author_sort | Tianpei Xu |
| collection | DOAJ |
| description | In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems. |
| first_indexed | 2024-03-10T11:10:42Z |
| format | Article |
| id | doaj.art-d03acf432e8546afaf21f822a54fa667 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:10:42Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d03acf432e8546afaf21f822a54fa6672023-11-21T20:49:00ZengMDPI AGApplied Sciences2076-34172021-05-011111474210.3390/app11114742Telecom Churn Prediction System Based on Ensemble Learning Using Feature GroupingTianpei Xu0Ying Ma1Kangchul Kim2Department of Computer Engineering, Chonnam National Unversity, Yeosu 59626, KoreaDepartment of Computer Engineering, Chonnam National Unversity, Yeosu 59626, KoreaDepartment of Computer Engineering, Chonnam National Unversity, Yeosu 59626, KoreaIn recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.https://www.mdpi.com/2076-3417/11/11/4742customer churnCRMmachine learningensemble learningfeature grouping |
| spellingShingle | Tianpei Xu Ying Ma Kangchul Kim Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping Applied Sciences customer churn CRM machine learning ensemble learning feature grouping |
| title | Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping |
| title_full | Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping |
| title_fullStr | Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping |
| title_full_unstemmed | Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping |
| title_short | Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping |
| title_sort | telecom churn prediction system based on ensemble learning using feature grouping |
| topic | customer churn CRM machine learning ensemble learning feature grouping |
| url | https://www.mdpi.com/2076-3417/11/11/4742 |
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