CHURN PREDICTION IN BANKING SYSTEM USING K-MEANS, LOF AND CBLOF
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Author:
IRFAN ULLAH
Citable URI :
https://vspace.vu.edu.pk/detail.aspx?id=336
Publisher :
Virtual University
Date Issued:
7/4/2020 12:00:00 AM
Abstract
Customer churn prediction helps in identifying those customers who are probable to stop a subscription, product or service, and is therefore very essential for any business. Churn prediction
can be very valuable for customer retention, as it helps in predicting customers that are at risk of
leaving. It is more challenging to put forth churn prediction in banking sector, as there is no
contractual agreement between a customer and the bank regarding the duration of services. Loss
of customers can be very costly as it is very expensive to obtain new customers in this age of
competition. There are many churn prediction techniques however; K-Means, Local Outlier
Factors (LOF) and Cluster-Based Local Outlier Factors (CBLOF) have not been used so far for
this purpose. In this research, I have applied these techniques for customer churn prediction. The
results are evaluated and analyzed using Precision (Pr), Recall (Re) and F1
measure to justify the efficiency and effectiveness of this research.
URI :
https://vspace.vu.edu.pk/details.aspx?id=336
Citation:
Ullah,I(2019).CHURN PREDICTION IN BANKING SYSTEM USING K-MEANS, LOF AND CBLOF. Virtual University of Pakistan(Lahore, Pakistan)
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Final Version
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