Using PCA to predict customer churn in telecommunication dataset

Authors:
T. Sato;B. Q. Huang;Y. Huang;M.-T. Kechadi;B. Buckley
Affiliations:
School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;Eircom Limited, Dublin 8, Ireland
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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Abstract

Failure to identify potential churners affects significantly a company revenues and services that can provide. Imbalance distribution of instances between churners and non-churners and the size of customer dataset are the concerns when building a churn prediction model. This paper presents a local PCA classifier approach to avoid these problems by comparing eigenvalues of the best principal component. The experiments were carried out on a large real-world Telecommunication dataset and assessed on a churn prediction task. The experimental results showed that local PCA classifier generally outperformed Naive Bayes, Logistic regression, SVM and Decision Tree C4.5 in terms of true churn rate.