An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry.
Churn prediction
Classification
Clustering
Decision support system
Hybrid model
Journal
PeerJ. Computer science
ISSN: 2376-5992
Titre abrégé: PeerJ Comput Sci
Pays: United States
ID NLM: 101660598
Informations de publication
Date de publication:
2022
2022
Historique:
received:
12
08
2021
accepted:
22
12
2021
entrez:
2
5
2022
pubmed:
3
5
2022
medline:
3
5
2022
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 35494841
doi: 10.7717/peerj-cs.854
pii: cs-854
pmc: PMC9044233
doi:
Banques de données
figshare
['10.6084/m9.figshare.18130610.v1']
Types de publication
Journal Article
Langues
eng
Pagination
e854Informations de copyright
© 2022 Fakhar Bilal et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
Références
PLoS One. 2021 Feb 25;16(2):e0245909
pubmed: 33630869
PeerJ Comput Sci. 2021 Aug 25;7:e645
pubmed: 34541306