Classification of Customer Churn Prediction Model for Telecommunication Industry Using Analysis of Variance
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Advanced Engineering and Science - International Journal of Artificial Intelligence, 12(3), 1323 – 1329
Abstract
Customer predictive analytics has shown great potential for effective churn
models. Thriving in today's telecommunications industry, discerning
between consumers who are likely to migrate to a competitor is enormous.
Having reliable predictive client behavior in the future is required. Machine
learning algorithms are essential to predict customer turnovers, and
researchers have proposed various techniques. Churn prediction is a problem
due to the unequal dispersal of classes. Most traditional machine learning
algorithms are ineffective in classifying data. Client cluster with a higher
risk has been discovered. A support vector machine (SVM) is employed as
the foundational learner, and a churn prediction model is constructed based
on each analysis of variance (ANOVA). The separation of churn data
revealed by experimental assessment is recommended for churn prediction
analysis. Customer attrition is high, but an instantaneous support can ensure
that customer needs are addressed and assess an employee's capacity to
achieve customer satisfaction. This study uses an ANOVA with a SVM,
classification in analyzing risks in telecom systems It may be determined
that SVM provides the most accurate forecast of customer turnover (95%).
The projected outcomes will allow other organizations to assess possible
client turnover and collect customer feedback.
Keywords:
Analysis of variance ,Churn, Machine learning , Support vector machine, Telecommunication