TY  - JOUR
TI  - Resampling and Ensemble Strategies for Churn Prediction
AB  - Churn analysis is a customer relationship management analytics that companies implement to predict the customers who are likely to terminate doing business with them. The success of marketing efforts to retain the existing customers is possible only if probable churners are correctly specified beforehand. Therefore, having powerful models with high prediction capabilities that lead to a profit growth is crucial. The imbalanced nature of churn datasets negatively effects the classification performance of machine learning methods. This study examines resampling –over- and under-sampling- and ensemble learning –bagging, boosting, and stacking– strategies integrated with the cross-validation procedure on imbalanced churn prediction. The experimental results, which are compared to the results of Support Vector Machines taken as the benchmark, show that ensemble methods improve the prediction performances. Also, applying over-sampling achieves a noticeable performance in comparison with the under-sampling approach.
AU  - ÇELIK, SERRA
AU  - Tolun Tayalı, Seda
DO  - 10.17671/gazibtd.1314870
PY  - 2023
JO  - Bilişim Teknolojileri Dergisi
VL  - 16
IS  - 4
SN  - 1307-9697
SP  - 263
EP  - 273
DB  - TRDizin
UR  - http://search/yayin/detay/1207884
ER  -