Churn prediction in telecommunication industry using kernel Support Vector Machines.
In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0267935 |
| _version_ | 1811290455364599808 |
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| author | Nguyen Nhu Y Tran Van Ly Dao Vu Truong Son |
| author_facet | Nguyen Nhu Y Tran Van Ly Dao Vu Truong Son |
| author_sort | Nguyen Nhu Y |
| collection | DOAJ |
| description | In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company. Baseline SVM models were initially built to find out the most suitable kernel types and will be used to make comparison with other approaches. Dimension reduction strategies such as Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) were applied to the dataset to find out the most important features. Furthermore, resampling techniques to deal with imbalanced data such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE Tomek) and Synthetic Minority Oversampling Technique ENN (SMOTE ENN) were used on the dataset. Using the above-mentioned techniques, we have obtained better results compared to those obtained from previous works, we achieved an F1-score and accuracy of 99% and 98.9% respectively. |
| first_indexed | 2024-04-13T04:13:09Z |
| format | Article |
| id | doaj.art-6bdccb6bfcb646bdb8b204308e2df1bd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T04:13:09Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6bdccb6bfcb646bdb8b204308e2df1bd2022-12-22T03:03:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026793510.1371/journal.pone.0267935Churn prediction in telecommunication industry using kernel Support Vector Machines.Nguyen Nhu YTran Van LyDao Vu Truong SonIn this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company. Baseline SVM models were initially built to find out the most suitable kernel types and will be used to make comparison with other approaches. Dimension reduction strategies such as Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) were applied to the dataset to find out the most important features. Furthermore, resampling techniques to deal with imbalanced data such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE Tomek) and Synthetic Minority Oversampling Technique ENN (SMOTE ENN) were used on the dataset. Using the above-mentioned techniques, we have obtained better results compared to those obtained from previous works, we achieved an F1-score and accuracy of 99% and 98.9% respectively.https://doi.org/10.1371/journal.pone.0267935 |
| spellingShingle | Nguyen Nhu Y Tran Van Ly Dao Vu Truong Son Churn prediction in telecommunication industry using kernel Support Vector Machines. PLoS ONE |
| title | Churn prediction in telecommunication industry using kernel Support Vector Machines. |
| title_full | Churn prediction in telecommunication industry using kernel Support Vector Machines. |
| title_fullStr | Churn prediction in telecommunication industry using kernel Support Vector Machines. |
| title_full_unstemmed | Churn prediction in telecommunication industry using kernel Support Vector Machines. |
| title_short | Churn prediction in telecommunication industry using kernel Support Vector Machines. |
| title_sort | churn prediction in telecommunication industry using kernel support vector machines |
| url | https://doi.org/10.1371/journal.pone.0267935 |
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