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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006166

Page Number

1168-1173

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Title

Prediction of Customer Churn in Telecom Industry Using Deep Learning Techniques

ISSN

2349-5162

Cite This Article

"Prediction of Customer Churn in Telecom Industry Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1168-1173, June-2020, Available :http://www.jetir.org/papers/JETIR2006166.pdf

Abstract

: Churn is the process, where a customer moves away or breaks off a subscription with his provider. Customer churn prediction performs a primary role in telecommunication industry due to the smart phone dominated era. It is very necessary to interpret and forecast the behaviour of customers. Achieving the new customer is more costly than engaging the old one. In previous study, machine learning techniques were used to forecast the client’s churn which comparatively produced less accuracy. The latest technologies in neural networks are CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) that commit the result to the problem by predicting the customer.

Key Words

churn prediction, telecommunication, CNN, RNN.

Cite This Article

"Prediction of Customer Churn in Telecom Industry Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1168-1173, June-2020, Available at : http://www.jetir.org/papers/JETIR2006166.pdf

Publication Details

Published Paper ID: JETIR2006166
Registration ID: 234146
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 1168-1173
ISSN Number: 2349-5162

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Cite This Article

"Prediction of Customer Churn in Telecom Industry Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1168-1173, June-2020, Available at : http://www.jetir.org/papers/JETIR2006166.pdf




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