## 1 Introduction  
Sustainable Quality of Service (QoS) for users is one of the main tasks of mobile operators. This orients them to provide comprehensive support for various applications and services with numerous QoS requirements in order to meet the expected levels of user Quality of Experience (QoE) [1, 2]. The development of Long Term Evolution (LTE) technology, which today is based on IP network configuration [3], is an example of such an orientation. The target reason is optimal performance, i.e., low delay and high data transfer speed, as well as better optimization of packet transfer. In addition to the mentioned key features of LTE network technology, there is Radio Resource Management (RRM), which can raise network performance almost to the level of the Shannon limit [4]. An important operational technology of LTE is Packet scheduling for assigning a part of the network's resources to each User Equipment (UE) depending on QoS requirements, but also on the impact of delay, channel quality, number of active UEs, throughput, etc. During network congestion, users' QoS requirements increase, and today popular interactive real-time services, such as Voice over IP (VoIP), i.e., Voice over Long Term Evolution (VoLTE), and streaming are the most sensitive and susceptible to degradation in that period. Key network performance indicators during congestion are end-to-end (E2E) delay and jitter, which represents variations in delay [5]. According to the standard 123 107 v12.0.0 (2014) of the European Telecommunications Standards Institute (ETSI) [6], the maximum tolerated delay for VoIP services is defined as 100 ms, and 300 ms for streaming services. End-to-end delay can be defined as the time required for a data packet to be transmitted through a network from a source node to a destination node, and in a VoIP network it consists of the sum of transmission delay, signal propagation delay and packet waiting delay. Current research in various fields shows that predictive models, which predict events and situations from the present towards the future based on data from the past, have an enormously wide range of applications. Predictive models are most often based on machine learning (ML) techniques, especially in telecommunications. The relevance and application of predictive models using ML techniques are encouraged by a very rapid increase in the amount of multidimensional data: Big Data (BD) publicly available on the Internet. BD increases the complexity of the problem of finding the optimal way to the solution to functional tasks in the network domain. At the same time, the high dimensionality of data, i.e., a large number of variables, often makes it difficult to create a model and jeopardizes the accuracy of prediction results. Among the discovered approaches to solutions for reducing the problem of complexity, data preprocessing by dimensionality reduction techniques is used. Complexity represents a key indicator of the state configuration in the situational dynamics of telecommunication traffic. Data dimensionality reduction implies optimization of the space of input/independent variables and the number of predictors, but with the obligation to preserve relevance and other qualitative attributes of information [7]. Feature Selection is one of the most common and important dimensionality reduction techniques, and, in research papers, it is also known as variable selection, attribute selection or variable subset selection. In this paper, the research focus is on three dimensionality reduction approaches: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. The first two approaches belong to Feature selection techniques. The selection of input variables is a process that includes the detection of variables that have a significant impact on the prediction of output, and the removal of redundant variables.