An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry.

ABSTRACT: Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individual

SUBMITTER: Fakhar Bilal S 

PROVIDER: S-EPMC9044233 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature