Customer Stress Prediction in Telecom Industries using Machine Learning (International Journal of Innovative Research in Engineering & Management, 2022)
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; One Hot Encoding