DETECTION OF CONCEPT OF DRIFT IN TELECOMMUNICATION STREAM

DETECTION OF CONCEPT OF DRIFT IN TELECOMMUNICATION STREAM

Harshitha L G1, Vinay patel G L2

[1]Student, Department of MCA, BIET, Davanagere

[2]Assistant Professor, Department of MCA, BIET, Davanagere

ABSTRACT

In industries like telecom, where understanding and anticipating client attrition can have a substantial impact on corporate stability and growth, customer churn prediction is a vital concern. This study examines several churn prediction approaches, highlighting how they depend on data mining and machine learning methods. Many models have been created by researchers, who frequently use metaheuristic techniques to improve prediction accuracy. Interestingly, hybrid models—which combine several approaches—have proven to be especially successful in providing accurate insights into customer behavior and supporting proactive retention measures. Telecom businesses can effectively reduce attrition by customizing their services and interventions to identify high-risk clients early on. This study emphasizes how crucial it is to develop churn prediction techniques in order to adjust to changing customer preferences and market conditions.

Keyword: stream data, data mining, machine learning.