###### Abstract  
The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of \(QoE\), this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal-regulatory, technological-process, content-formatted and performative, contextual-relational and subjective user-influencing factors on \(QoE\) sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark \(QoE\) as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of \(QoE\). The hypothetical setting is that the individual user's curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable \(QoE\) model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the \(QoE\) assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of \(QoE_{i}\). The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%. QoS; sustainable quality \(QoE_{i}\); paired components; influence factors; transition variables in the model; user satisfaction and rigidity; DA; prediction; classification; machine learning models +
Footnote †: journal: _Sustainability_  
of mobility. Therefore, \(QoE\) represents one of the most important criteria when choosing a provider of telecommunication services today. On the other hand, not only is it also very important for telecommunication operators to understand the ways of user perception and estimation of \(QoE\) in order to achieve a competitive advantage, but knowledge of user \(QoE\) can also be a very important reference for optimizing network resources. In addition to subjective measurements of \(QoE\) based on user ratings, providers must perform objective measurements of quality of service (QoS) through certain parameters, and, by improving them, influence the increase in the level of \(QoE\)[1]. It is especially important to predict user needs for a certain level of \(QoE\) in the future, which is achieved via predictive and classification models [2] based on machine learning techniques, such as artificial neural networks [3]. Predictive \(QoE\) modeling can be descriptively defined as a broad term that refers to the process of developing a mathematical tool or model that generates an accurate forecast based on existing historical data. Therefore, it can be said that predictive modeling facilitates the achievement of sustainability, which according to [4] is viewed as a main normative principle for modern society that includes the long-term ethical relationship of current generations with the generations of the future.