Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs)

Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/27151

Title:	Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs)
Authors:	A. Abdalla, Faroug E Osman Ali, Saife
Keywords:	Support vector machines (SVMs) Decision trees (DTs Data mining Call detail records (CDRs), Supervised Machine Learning (SLM) Total Contribution (T.C).
Issue Date:	10-Apr-2021
Publisher:	Sudan University of Science and Technology
Citation:	A. Abdalla Faroug, Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs), Faroug A. Abdalla, Saife E Osman Ali- Journal of Engineering and Computer Sciences (ECS) .- Vol .22 , no3.- 2021.- article
Abstract:	On a daily basis, telecom businesses create a massive amount of data. Decision-makers underlined that acquiring new customers is more difficult than maintaining current ones. Further, existing churn customers' data may be used to identify churn consumers as well as their behavior patterns. This study provides a churn prediction model for the telecom industry that employs SVMs and DTs to detect churn customers. The suggested model uses classification techniques to churn customers' data, with the Support Vector Machine (SVMs) method performing well 98.36 % properly categorized instances) and the Decision Tree (DTs) approach performing poorly 33.04 % and the decision tree algorithm deliver outstanding results
URI:	http://repository.sustech.edu/handle/123456789/27151
Appears in Collections:	Volume 22 No. 3

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