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<title>Department of Computing and Information Systems</title>
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<description>Scholarly work produced by the members of the Department of Computing and Information Systems</description>
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<dc:date>2026-05-07T11:26:15Z</dc:date>
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<title>Customer Churn Analysis and Prediction in Telecommunication for Decision Making</title>
<link>http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1762</link>
<description>Customer Churn Analysis and Prediction in Telecommunication for Decision Making
Alwis, P.K.D.N.M.; Kumara, B.T.G.S.; Hapuarachchi, H.A.C.S.
With the rapid development of communication&#13;
technology, the field of telecommunication faces complex&#13;
challenges due to the number of vibrant competitive service&#13;
providers. Customer Churn is the major issue that faces by the&#13;
Telecommunication industries in the world. Churn is the activity&#13;
of customers leaving the company and discarding the services&#13;
offered by it, due to the dissatisfaction with the services. The&#13;
main areas of this research contend with the ability to identify&#13;
potential churn customers, cluster customers with similar&#13;
consumption behavior and mine the relevant patterns embedded&#13;
in the collected data. The primary data collected from customers&#13;
were used to create a predictive churn model that obtain&#13;
customer churn rate of five telecommunication companies. For&#13;
model building, classified the relevant variables with the use of&#13;
the Pearson chi-square test, cluster analysis, and association rule&#13;
mining. Using the Weka, the cluster results produced the&#13;
involvement of customers, interest areas and reasons for the&#13;
churn decision to enhance marketing and promotional activities.&#13;
Using the Rapid miner, the association rule mining with the FPGrowth&#13;
component was expressed rules to identify&#13;
interestingness patterns and trends in the collected data have a&#13;
huge influence on the revenues and growth of the&#13;
telecommunication companies. Then, the C5.0 Decision tree&#13;
algorithm tree, the Bayesian Network algorithm, the Logistic&#13;
Regression algorithm, and the Neural Network algorithms were&#13;
developed using the IBM SPSS Modeler 18. Finally, comparative&#13;
evaluation is performed to discover the optimal model and test&#13;
the model with accurate, consistent and reliable results.
for correspondences: &#13;
P.K.D.N.M. Alwis&#13;
Department of Computing and&#13;
Information Systems&#13;
Sabaragamuwa University of Sri&#13;
Lanka&#13;
Belihuloya, Sri Lanka,&#13;
madushani.niroshi@gmail.com
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<dc:date>2018-08-26T00:00:00Z</dc:date>
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