A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models

Abstract

For organizations across a range of industries, customer churn is a serious problem.Businesses must identify consumers who are in danger of leaving in order totake actions to keep them. By examining previous data and seeing trends indicatingclient behavior, machine learning provides a potent remedy for forecastingcustomer attrition. In this article, the investigation of the way machine learningtechniques can be utilized to forecast consumer attrition in the telecommunicationindustry has been done. The machine learning models decision trees and randomforests have been trained and assessed using IBM’s dataset of customer behaviorand demographics. These models are compared using performance parameterslike recall, accuracy, F1-score, and precision. According to chosen data, the randomforest model exceeds the competition with a remarkable accuracy of 96%.This study demonstrates the potential of machine learning in predicting customerturnover and offers insightful information for creating strategies that effectivelyretain customers.