This dataset is being shared for the first time for public research after extensive research performed. See the following publications for more information.

• Jafari-Marandi, R., Denton, J., Idris, A., Smith, B. K., & Keramati, A. Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry. Neural Computing and Applications, 1-34.
• Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in the telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994-1012.
• Keramati, A., & Ardabili, S. M. (2011). Churn analysis for an Iranian mobile operator. Telecommunications Policy, 35(4), 344-356.

This dataset is perfect for practicing prescriptive analysis such as predictive prescription or predictive decision making. The reason is that the dataset has the attribute of customer value which allows for creating False Positive (FP) and False Negative(FN) costs in case of misclassification. In standard classification tasks, it is assumed that FPs and FNs are the same, which is not the case for many cases. Furthermore, even if it is recognized that FPs and FNs are indeed different, their different balances per each data object are not understood or taken into consideration. This dataset gives you the opportunity to create a model that recognizes these complexities. For further information about the balance of FPs and FNs see the first mentioned publication. Also, you can find more information about each attribute on one of the publications.

Dataset

This dataset was created by R. Joseph Manoj, PhD

Contents

Dataset

This dataset was created by Shiyamaladevi R S

Contents