Customer Stress Prediction in Telecom Industry using Machine Learning

D. Janardhan Reddy; V. Gopi Krishna; Sk. JIlani Basha; R. Pavan Kumar; K. Manohara Rao

doi:10.55524/ijirem.2022.9.4.20

Abstract

In today's highly competitive marketplace, especially for businesses, retaining loyal customers is becoming increasingly challenging.In one scenario, losing a customer results in a loss of profits for the telecom industry's expansion, while in another, the cost of acquiring new customers is significantly higher than the cost of maintaining existing ones; in this critical scenario, the telecom industry ought to concentrate on maintaining existing customers.This project will use supervised machine learning algorithms, primarily Linear Discriminant Analysis, Support Vector Machine, K Nearest Neighbor, and Random Forest, to analyze the open dataset of customer data and predict customer stress.

Keywords

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

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Cites this article as

D. J. Reddy, V. G. Krishna, S. J. Basha, R. P. Kumar, K. M. Rao, "Customer Stress Prediction in Telecom Industry using Machine Learning", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-4, Page No-118-121, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.4.20