Predicting Termination of Mobile Subscribers Using Classification Algorithms

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Addis Ababa University


Service termination is one key challenge of operators that can significantly reduce their revenue. It is still a challenge even in Ethiopian monopoly market even if users do not have alternative service provider. For instance, the market has seen 1.6 percent overall prepaid mobile subscriber termination rate within a quarter, let alone specific service termination. To address this challenge, operators need to understand causes of termination and take timely proactive actions to mitigate number of terminations. For this purpose, they need to accurately and timely predict subscribers with potential service termination based on collected service related data. Performance of various classification algorithms have been investigated to predict subscribers’ behavior. However, performance of such algorithms are not studied in the context of Ethiopia where ethio telecom has an enormous data that helps to define its subscribers’ behavior. Objective of this thesis work is to study performance of classification learning algorithms for predicting termination in the context of Ethiopian prepaid subscribers. Studied algorithms are J48, Random Forest and Naïve Bayes and their performance are compared in terms of prediction accuracy, performance, errors and interpretability of their model. Algorithms effectiveness and efficiency are evaluated considering two validation methods (Percentage Split and Cross Validation) and three data sets. For the performance evaluation, we used WEKA 3.8 tool algorithm implementation. Obtained results show that Random Forest scores the highest prediction accuracy while Naïve Bayes scores the least. Random Forest and Naïve Bayes scores their best at 93.4% and 85.9% respectively, besides J48 scores 93.3%. J48 is as accurate & robust as Random Forest. Moreover, it provides the most interpretable and clear model.



J48, Random Forests, Naïve Bayes, service termination, subscribers’ defection, churn, confusion matrix