Customer Churn Prediction

Authors

  • Dr. D. Esther Rani  Associate Professor, Department of CSE, N.B.K.R. Institute of Science & Technology Vidyanagar, Tirupati, Andhra Pradesh, India
  • Chaitanya T  B.Tech Students, Department of CSE, N.B.K.R. Institute of Science & Technology Vidyanagar, Tirupati, Andhra Pradesh, India
  • Jasmine Sk  B.Tech Students, Department of CSE, N.B.K.R. Institute of Science & Technology Vidyanagar, Tirupati, Andhra Pradesh, India
  • Maheshwari T  B.Tech Students, Department of CSE, N.B.K.R. Institute of Science & Technology Vidyanagar, Tirupati, Andhra Pradesh, India
  • Sai Geethika V  B.Tech Students, Department of CSE, N.B.K.R. Institute of Science & Technology Vidyanagar, Tirupati, Andhra Pradesh, India

Keywords:

Supervised learning, Machine Learning, Random Forest Algorithm and Support Vector Machines.

Abstract

Customers are becoming more drawn to the standard of service (QoS) offered by businesses in the present. However, the present day shows greater rivalry in offering clients technologically cutting-edge QoS. However, efficient communication systems may help the organization attract new clients, preserve client connections, and enhance client retention by generating more revenue for the company's operations. Additionally, the client retention methods can benefit greatly from the use of machine learning models like support vector machines and Random Forest algorithms.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. D. Esther Rani, Chaitanya T, Jasmine Sk, Maheshwari T, Sai Geethika V, " Customer Churn Prediction" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.398-406, May-June-2023.