A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms
INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that in...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2022-07-01
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| Series: | EAI Endorsed Transactions on Mobile Communications and Applications |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/mca/article/view/2181 |
| _version_ | 1811245411564781568 |
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| author | Sulaiman Olaniyi Abdulsalam Jumoke Falilat Ajao Bukola Fatimah Balogun Micheal Olaolu Arowolo |
| author_facet | Sulaiman Olaniyi Abdulsalam Jumoke Falilat Ajao Bukola Fatimah Balogun Micheal Olaolu Arowolo |
| author_sort | Sulaiman Olaniyi Abdulsalam |
| collection | DOAJ |
| description |
INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn.
OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn.
METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset.
RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier.
CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.
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| first_indexed | 2024-04-12T14:38:38Z |
| format | Article |
| id | doaj.art-2e983742b86848de955cb2469c6af9e3 |
| institution | Directory Open Access Journal |
| issn | 2032-9504 |
| language | English |
| last_indexed | 2024-04-12T14:38:38Z |
| publishDate | 2022-07-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Mobile Communications and Applications |
| spelling | doaj.art-2e983742b86848de955cb2469c6af9e32022-12-22T03:28:57ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Mobile Communications and Applications2032-95042022-07-0172110.4108/eetmca.v6i21.2181A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network AlgorithmsSulaiman Olaniyi Abdulsalam0Jumoke Falilat Ajao1Bukola Fatimah Balogun2Micheal Olaolu Arowolo3Kwara State University Kwara State University Kwara State University Landmark University INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients. https://publications.eai.eu/index.php/mca/article/view/2181TelecomsChurnRelief-FCNNRandom Forest |
| spellingShingle | Sulaiman Olaniyi Abdulsalam Jumoke Falilat Ajao Bukola Fatimah Balogun Micheal Olaolu Arowolo A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms EAI Endorsed Transactions on Mobile Communications and Applications Telecoms Churn Relief-F CNN Random Forest |
| title | A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms |
| title_full | A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms |
| title_fullStr | A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms |
| title_full_unstemmed | A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms |
| title_short | A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms |
| title_sort | churn prediction system for telecommunication company using random forest and convolution neural network algorithms |
| topic | Telecoms Churn Relief-F CNN Random Forest |
| url | https://publications.eai.eu/index.php/mca/article/view/2181 |
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