## 5 Conclusions  
For a long period of time, not only the amount of data, called BD, but also the number of users of network services and the range of user requests for higher QoS has been increasing drastically. Telecommunications operators face increasingly complex technical and technological problems in a domain of network traffic management, adequate planning and modern design of all dimensions of network resource quality, including their allocation and performance--KPI. This is especially important for services such as VoIP and traffic streaming. Predictive modeling of required solutions currently is most often based on the techniques of the ML method. Numerous studies of different approaches to certain solutions for indicated problems are analyzed in this paper and presented in Section 2. Using the above and other experiences and theoretical findings of more comprehensive studies, the paper presents original approaches to predictive modeling of end-to-end delay of data packets through a real 4G LTE network. The network is in the geo-space covered by the M:tel BL mobile operator with a focus on the area of a three-segment road in the road network of RS, BiH. In the LTE architecture, a total of 87 cells are located in the observed area, which provide users with a continuous and permanent network connection. In the paper, the aims and objectives of the research have been fulfilled. It includes reducing the dimensionality of the space of input variables in the optimization model with Feature Selection techniques (RReliefF and Backward selection via the recursive feature  
Figure 14: Scatter plot of actual \(\mathrm{D_{EtoE}}\) values and \(\mathrm{D_{EtoE}}\) values obtained by prediction using the final k-NN model. elimination algorithms) and the Pareto 80/20 rule. It is followed by training and testing of ML models with MLP, SVM and k-NN techniques including the selection of the best delay prediction model in the LTE network according to criteria of accuracy and complexity/interpretability. Then, the implementation of a unique methodology of indirect assessment and calculation of dependent variable values based on the average number of active users in the network has been performed. At the same time, a universally applicable predictive model of delay in the LTE network, based on the research in the real space of Big Data (BD) with input-output vectors, has been created. In the opinion of the team of authors, the presented approaches to the optimization of the number of predictors by end-to-end delay ML modeling techniques in LTE networks by reducing the dimensions of BD and connecting independent variables in pairs with the calculation of KPI are a particularly important innovative contribution to the research of telecommunications traffic provided in this paper. It also involves the methodology of presenting and interpreting textual, algorithmic, graphic, photo-documentation, mathematical and computer-generated solutions. An optimal explanatory strategy has also been used in creating a system of clarification of the presented methodology and results referring to similarities in the structure of what is being investigated in this paper with already known facts that, among other things, were published in the cited papers and other authors' solutions. Also, familiar systems of relations that are used as models which can be useful to understand the new experience in the systematic scientific research of telecommunications traffic are taken into account, and the similarities created in analogies and in hypotheses have led to the proven quality of the results presented. The research results show that the k-NN model has been selected as the best solution in all three approaches to the optimization of the input set of variables. For the RReliefF optimization algorithm, the best model has 6 inputs and \(RE=0.109\); for Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and \(RE=0.041\); and for the Pareto 80/20 rule, the best model has 11 inputs and \(RE=0.049\).