## 3 Materials and Methods
### Creating a Predictive Model--Digital Data Treatment
In this paper, the MLP model was created in the IBM SPSS Statistics 22 software that uses nonlinear modeling to detect complex relationships between the collected data. It enables the creation of machine learning models, integration with Big Data and seamless deployment into applications. The process of creating an MLP model based on an initial set of 71,053 input/output vectors, provided by a vendor, can be represented algorithmically in the following successive steps (Figure 1):  
* Removal of unusual and missing values to obtain a final set of 64,301 input/output vectors;
* Rescaling the values of 17 input variables;
* Selection of the ratio between the training data set and the testing data set (30%:70%, 40%:60%, 50%:50%, 60%:40%, and 70%:30%);
* Selection of MLP model metaparameters (number of neurons in hidden layers: 10, 10; 20, 20; 30, 30; output function of hidden layer neurons: Hyperbolic tangent or Sigmoid);Selection of MLP model training metaparameters (online learning, backpropagation algorithm, initial learning rate = 0.4, lower learning rate limit = 0.001, number of epochs--automatic);
* Selection of training stop criteria (15 min or five iterations without reducing the error function);
* Choice of how to present results (tabular, graphical results);
* Training and testing of MLP model;
* Prediction results: relative error (RE), degree of accuracy, coefficient of determination (R\({}^{2}\));
* For each of the 30 different variants of the MLP model, training and testing are performed three times;
* Selection of the best final model based on the average value for three measurements;
* Selection and presentation of the best individual prediction results for both dependent variables;
* Comparison of MLP model prediction performance with the created MLR model. Figure 1: Algorithm for creating MLP model. According to the above steps of the algorithm, the training and testing of several different MLP models were performed, with different combinations of the metaparameters. On the data set for testing, the validation of each individual model was performed, i.e., assessment of its quality based on relative criteria: RE and R2, which are dimensionless quantities. In general, the relative error can be defined as the ratio of absolute error, which is obtained as the difference between the value of the model prediction and the actual value (real value from the data set), according to the actual value of the observed variable:  
\[\text{RE}=\Big{|}\frac{\text{Predicted Value }-\text{ Real Value}}{\text{Real Value}}\Big{|}. \tag{1}\]  
This type of error is relative to the actual value of the variable, has no unit of measure and can be expressed as a percentage. In this study, the two dependent variables, _Cell Downlink Average Throughput_ and _Average User Downlink Throughput_, are continuous in nature, meaning that the RE of MLP model training and testing represents the total, average sum of RE values, represented by expression (1), for all measurements. Based on the RE, another parameter of the model quality can be defined, and that is the degree of accuracy of the model which is equal to 1-RE (%). Based on the above indicators, the best final model was selected, the results of which are presented in Section 5.