Customer Stress Prediction in Telecom Industries using Machine Learning

Dr. Pathan Hussain Basha; V.Sriharsha; S. Giri Babu

doi:10.55524/ijirem.2022.9.5.31

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.

Keywords

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

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Cites this article as

D. P. H. Basha, V.Sriharsha, S. G. Babu, "Customer Stress Prediction in Telecom Industries using Machine Learning ", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-5, Page No-219-222, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.5.31