Machine Learning as Intelligent tool for Churn Prediction in Telecommunication Industry

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Megha Gupta, Anju Bhandari Gandhi, S. C. Gupta

Megha Gupta, Anju Bhandari Gandhi and S C Gupta. Machine learning as intelligent tool for churn prediction in Telecommunication Industry. International Journal of Computer Applications 181(10):16-22, August 2018. BibTeX

	author = {Megha Gupta and Anju Bhandari Gandhi and S. C. Gupta},
	title = {Machine learning as intelligent tool for churn prediction in Telecommunication Industry},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {10},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {16-22},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2018917621},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In most of the sectors dominated by the cost of changing the landscape, wear of the activities is painted or blurred by the clients (clients who wish to modify or change their suppliers for various reasons). This phenomenon is widespread in the telecommunications industry and all aspects related to its lead to the belief that it increases sharply. Since the market is very competitive and the number of prepaid customers, "it increases, it is vital that companies to actively confront with the distraction of customers, the identification of behaviour that could ultimately be created." In this article we have proposed A hybrid model machine learning to predict distortion in mobile telecommunications networks The experiments were carried out using a variety of tools for machine learning, together with a set of real data from the open data provider to evaluate the manufacturer's productivity. The results show that the new hybrid model is more accurate than the individual methods. Auto learning is a subset of artificial intelligence, commonly used statistical techniques to give computers the ability to "learn" the data.


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Churn prediction, logistic regression, and telecommunications, voted perceptron.