Applying over 100 classifiers for churn prediction in telecom companies
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Applying over 100 classifiers for churn prediction in telecom companies Debjyoti Das Adhikary 1 & Deepak Gupta 1 Received: 25 February 2020 / Revised: 11 August 2020 / Accepted: 18 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In today’s date where machine learning is the key to solve so many problems in different fields, one really should know the extent of its importance in their field. One of the major applications of machine learning is Predictive Analytics. Churn prediction is one of the key steps for customer retention in this saturating market scenario [31]. This is one of the major objectives and any toolkit which can give insights on this can be really beneficial for any service providing companies. Furthermore, one of the major problems that business analysts face during this procedure is to decide which classifier to select. In the continuously evolving field of machine learning where developers are constantly coming up with new machine learning algorithms, it is often difficult for the analysts to have knowledge about the varied options. In our work, we try to analyze and compare the performance of over 100 classifiers in churn prediction of a telecom company. We have used renowned classifiers from different families. This work can serve as the first step for any data scientist who wants to develop a churn prediction system for their application. Also, we try to explore efficient algorithms that will give a better result. Churn prediction is a mildly imbalanced set of the problem which degrade the performance of classifiers. The highest accuracy is given by the Regularized Random Forest classifier. Since the problem is imbalanced, we also consider the area under the Receiver Operating Characteristic (ROC) curve and the classifier Bagging Random Forest produces the best result in this scenario. Keywords Classification . Customer churn prediction . Predictive analytics . Business intelligence . Operational research
* Deepak Gupta [email protected]; [email protected] Debjyoti Das Adhikary [email protected]
1
Department of Computer Science & Engineering, National Institute of Technology, Yupia, Arunachal Pradesh, India
Multimedia Tools and Applications
1 Introduction In any service providing company, a customer becomes a “churner” when they discontinue their subscription and move to a competitor service provider. In other words, churning is the process of customer turnover. This is a major issue for companies where a huge fraction of customers can easily switch to other competitors in the market. Examples include banking, insurance companies, telecommunication companies and gaming companies [46]. In the field of business intelligence, customer retention is one of the key objectives [31]. The limited number of customers leads to the saturation point in the market which makes customer acquisition much costlier than customer retention. It has been found that in this current market scenario, customer retention needs to be focused more than customer ac
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