Most of telecommunication companies consider the customer as the most important asset for them. For that reason, nowadays, a challenging problem that encounters telecommunication companies is when the customer leaves the company to another service provider for a reason or another . In most cases, this churn can happen in rates which seriously affect the profitability of the companies since it is easy for the customers to switch companies.
In market, where the competition between the telecommunication companies grows rapidly, companies have shifted their focus from acquiring new customers to retain their existing ones [1–3]. Basically, churn is one of these significant problems and companies started to seek new Business Intelligence (BI) applications that predict churn customers. When the company is aware of the percentage of customers who leave for another company in a given time period, it wouldbe easier to come up with a detailed analysis of the causesfor the churn rate and understand the behavior of customersthat unsubscribe and move to other business competitor. Thishelps in planning effective customer retention strategies for that company .
Among many approaches developed in the literature for predicting customer churn, supervised Machine Learning (ML) techniques are the most widely investigated [5–9]. Supervised ML concerns the developing of models whichcan learn from labeled data. ML includes a wide rangeof algorithms such as Decision trees, k-nearest neighbors,Linear regression, Naive Bayes, Neural networks, Supportvector machines (SVM), Genetic Programming and many others.
For example, in  authors conducted a comparative analysis of linear regression and two machine learning techniques; neural netwo...
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S.-Y. Hung, D. C. Yen, and H.-Y. Wang, “Applying data miningto telecom churn management,” Expert Systems with Applications,vol. 31, no. 3, pp. 515–524, 2006.
P. C. Pendharkar, “Genetic algorithm based neural network approachesfor predicting churn in cellular wireless network services,” Expert Syst.Appl., vol. 36, pp. 6714–6720, Apr. 2009.
Joseph P. Bigus. 1996. Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support. McGraw-Hill, Inc., Hightstown, NJ, USA.
L. V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall, 1994.
A. SUNG, “Ranking importance of input parameters of neural networks,” Expert Systems with Applications, vol. 15, no. 3--4, pp. 405 – 411, 1998.
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