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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.
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