Paper Info
Title
Mobile service experience prediction using machine learning methods.
Abstract
With the introduction of 4.5G, mobile operators have focused their efforts, infrastructure investments, tariffs and advertisements on the improvement of mobile data rates and services. Mobile services provided by mobile operators are influenced by various factors like the regional coverage of the operator, usage traffic, time and weather conditions. As a result, there may be differences between the quality of mobile services that the operators offer to their customers and those that the customers can actually access. The purpose of this study is to suggest a modelling approach for the prediction of the mobile service types that customers can experience based on machine learning techniques. To do this, based on 2017 speed tests data of three operators, alternative classification models are constructed for the prediction of the mobile service type. By comparing the performances of the models, best classification models were determined for different service categories. Using the data obtained from mobile speed tests performed on a limited number of locations, the models developed here enable the prediction of the possible service types that customers can experience in all locations in which the operators serve.
Year
Venue
Keywords
2018
Signal Processing and Communications Applications Conference
Machine Learning,Big Data,Mobile Services,Customer Experience,Prediction
Field
DocType
ISSN
Data modeling,Computer science,Support vector machine,Mobile service,AC power,Operator (computer programming),Artificial intelligence,Mobile broadband,Machine learning
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Ibrahim Onuralp Yigit100.34
Selami Ciftci2264.58
Feyzullah Alim Kalyoncu300.34
Tolga Kaya41368.63