## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **WSS** & **BSS** & **SILHOUETTE** \\ \hline \multicolumn{4}{c}{Two clusters} \\ \hline KM & 18.98391 & 51.73605 & 0.594664 \\ \hline AG & 19.20556 & 51.45037 & 0.561417 \\ \hline SP & 19.14204 & 51.58659 & 0.583539 \\ \hline GM & 19.46392 & 52.19415 & 0.58729 \\ \hline \multicolumn{4}{c}{Three clusters} \\ \hline KM & 15.23908 & 43.95228 & 0.475616 \\ \hline AG & 18.88447 & 38.80243 & 0.328751 \\ \hline SP & 15.27047 & 48.03171 & 0.376685 \\ \hline GM & 15.20677 & 45.76189 & 0.46373 \\ \hline \multicolumn{4}{c}{Four clusters} \\ \hline KM & 15.96251 & 37.9217 & 0.37866 \\ \hline AG & 16.93177 & 38.9466 & 0.281695 \\ \hline SP & 16.99529 & 43.89783 & 0.141275 \\ \hline GM & 16.514938 & 39.45284 & 0.35762 \\ \hline \multicolumn{4}{c}{Five clusters} \\ \hline KM & 15.51169 & 38.93565 & 0.182779 \\ \hline AG & 16.49797 & 35.55767 & 0.257393 \\ \hline SP & 15.07748 & 44.11309 & 0.07991 \\ \hline GM & 14.68248 & 39.96776 & 0.26404 \\ \hline \end{tabular}
\end{table} 
Table 4: Clustering evaluation metrics values. In accordance with the results of comparing the selection of different numbers of data clusters using the considered models, it was found that the Agglomerative clustering model is not applicable within the framework of the task. The non-applicability of the AG model is expressed visually and numerically. It can be seen in Figure 4 that the AG model provides results featuring one small-sized cluster mixed inside of another bigger cluster in cases where the number of clusters is greater than two. This specific behavior is also expressed in the values of all the metrics used for evaluation in Table 4. The KMeans Clustering, Spectral Clustering and GaussianMixture models showed similar results when identifying different clusters in the same cluster. Figure 4: Clustering results. numbers of data clusters (2-5). At the same time, it was determined that the selection of three data clusters in this problem is the optimal solution (the silhouette coefficient is the maximum, and WSS and BSS have the greatest difference). The averaged separation of KMeans Clustering, SpectralClustering, and GaussianMixture models was adopted as the final model to provide realistic but clearer borders of the identified data clusters. As a result of clustering, three data clusters of the following dimension were identified:  
* First cluster--2120 data lines;
* Second cluster--1977 data lines;
* Third cluster--1588 data lines. Selected clusters might be considered balanced, since the largest difference between the volumes of the first and third clusters is no more than 25% relative to the cluster with the largest amount of data. In accordance with the identified data clusters, three datasets were generated for training regression models to predict the generation of SPPs. A separate dataset was obtained from the original data by randomly selecting samples. This set was limited in size to accommodate the sample sizes based on the identified clusters. Thus, 1980 data samples were randomly selected from the original data, which corresponds to the volumes of the selected clusters. Each dataset was divided into training and testing sets in a ratio of 80/20. Information on the sampling structure for each of the three sets is presented in Table 5.  
Based on the comparison results (Figures 5-8 and Tables 6-9), Random Forest Regression was selected as a predictive model for further tuning. As a result of tuning the models using the Exhaustive search method in order to increase accuracy, four sets of parameters for predictive models were identified:  
* General model: ('criterion': 'poisson','max_features': 4, 'n_estimators': 115);
* Model for the first cluster: ('criterion': 'poisson','max_features': 4, 'n_estimators': 87);
* Model for the second cluster: ('criterion': 'poisson','max_features': 4, 'n_estimators':73);
* Model for the third cluster: ('criterion': 'poisson','max_features': 4, 'n_estimators':132).