Abstract
Customer churn is an important issue and major concern for many companies. This trend is more noticeable in Telecom field. Telecom operators requires an essential proactive method to prevent customer churn. The existing works fails to adopt best feature selection for designing model. This works contributes on developing churn prediction model, which helps telecom operators to identify the customers who are about to churn. The significance for the recall evaluation measure, which actually solves the real-time business problem is highlighted. The prominent goal of this churn analysis is to perform binary classification with customer records and figure out who are likely to cancelled in the future. Fifteen machine learning methods with different parameters are employed. The performance of the model is evaluated by various measures like Accuracy, Recall, Precision, F-score, ROC and AUC. Our aim in this work is to produce highest recall value which has a direct impact on real-world business problems. Based on experimental analysis, we observed that Decision Tree model 3 outperforms all other models.
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Pamina, J., Beschi Raja, J., Sam Peter, S., Soundarya, S., Sathya Bama, S., Sruthi, M.S. (2020). Inferring Machine Learning Based Parameter Estimation for Telecom Churn Prediction. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_30
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