@article{(Open Science Index):https://publications.waset.org/pdf/10011571,
	  title     = {Churn Prediction for Telecommunication Industry Using Artificial Neural Networks},
	  author    = {Ulas Vural and  M. Ergun Okay and  E. Mesut Yildiz},
	  country	= {},
	  institution	= {},
	  abstract     = {Telecommunication service providers demand accurate
and precise prediction of customer churn probabilities to increase the
effectiveness of their customer relation services. The large amount of
customer data owned by the service providers is suitable for analysis
by machine learning methods. In this study, expenditure data of
customers are analyzed by using an artificial neural network (ANN).
The ANN model is applied to the data of customers with different
billing duration. The proposed model successfully predicts the churn
probabilities at 83% accuracy for only three months expenditure data
and the prediction accuracy increases up to 89% when the nine month
data is used. The experiments also show that the accuracy of ANN
model increases on an extended feature set with information of the
changes on the bill amounts.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {14},
	  number    = {11},
	  year      = {2020},
	  pages     = {396 - 399},
	  ee        = {https://publications.waset.org/pdf/10011571},
	  url   	= {https://publications.waset.org/vol/167},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 167, 2020},