Customer Stress Prediction in Telecom Industries using Machine Learning

Authors

  • Pathan Hussain Basha Professor, Department of Computer Science & Engineering, PACE Institute of Technology & Sc Author
  • V Sriharsha Assistant.Professor, Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Padesh, India Author
  • S Giri Babu Associate.Professor, Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Padesh, India Author

Keywords:

Customer Stress, Support Vector Machine, K-Nearest Neighbor, Linear Discriminant Analysis, One Hot Encoding

Abstract

In the competitive world especially in  enterprises market maintaining valuable customers is  becoming a difficult task. In one situation losing a customer  is like decrease in profits for telecom industry growth, in  another situation the cost of acquiring new customers is  much higher than the cost of retaining the existing  customers, for this critical situation the telecom industries  should focus on retaining existing customers. This project  will analyze the customer data which was collected as open  dataset and predict the customer stress by applying  supervised machine learning algorithms mainly using  Linear Discriminant Analysis, Support Vector Machine, K  Nearest Neighbor and Random Forest. 

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Published

2022-10-30

Issue

Vol. 9 No. 5 (2022): International Journal of Innovative Research in Engineering & Management (Oct) 2022

Section

Articles

How to Cite

Customer Stress Prediction in Telecom Industries using Machine Learning . (2022). International Journal of Innovative Research in Engineering & Management, 9(5), 219–222. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/10755