## 3 Results
Descriptive statistics of the selected actual data that provide the generation of the estimated data and the estimated data based on machine learning algorithms are presented in Table 6. When the average values are examined, very close results are obtained between _Po_ and the prediction data; the average values of all algorithms vary around 0.385. This shows that the prediction algorithms model the general trend correctly. The standard error (SE Mean) and standard deviation (StDev) values are consistent between the prediction and actual data. Nevertheless, the lower standard deviation value for LR shows that this algorithm captures the spread in the data in a more limited way. In addition, the variance values are lower for LR, indicating that this algorithm offers less variability in its predictions than others. In terms of minimum and maximum values, it is noteworthy that the estimated values of all algorithms are pretty close to the actual data. Nevertheless, the GB algorithm has a negative minimum value (\(-0.093\)) in the estimated data. This may indicate that the estimates may sometimes shift to unrealistic values. While the quartile values (1 st, 2 nd, and 3 rd quartiles) do not show a significant difference between the estimated and actual data, a higher 2 nd and 3 rd quartile value was observed in LR estimates. The skewness and kurtosis results show that all estimation algorithms, except LR, exhibit a similar distribution to the actual data. The lower skewness and kurtosis values of LR indicate that the estimates of this algorithm have fewer extreme values and show a more normal distribution. These findings are essential in evaluating how successfully each algorithm captures different data features. Performance measurement values for the reliability of the estimated data obtained from machine learning algorithms are calculated and shared in Table 7. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R\({}^{2}\) measures used to evaluate the performance of the machine learning algorithms were calculated. The results show that the RF and GB algorithms stand out compared to other algorithms in estimation performance. These two algorithms have the lowest MSE and RMSE values (RF: 0.003, 0.053; GB: 0.003, 0.058), which shows that the amount of error in the estimates is relatively low. In addition, the R\({}^{2}\) values of RF and GB (0.988 and 0.985, respectively) are at the highest level, indicating that these algorithms are highly successful in explaining the variance of the dependent variable. MAE values are also at the lowest levels for RF and GB (RF: 0.036, GB: 0.040), indicating that the estimates are pretty close regarding the average absolute distance to the actual values. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Po (MW)** & **RF** & **kNN** & **GB** & **LR** & **Tree** \\ \hline Total Count & 31,586 & 31,586 & 31,586 & 31,586 & 31,586 & 31,586 \\ Mean & 0.385 & 0.385 & 0.381 & 0.385 & 0.386 & 0.385 \\ SE Mean & 0.003 & 0.003 & 0.003 & 0.003 & 0.002 & 0.003 \\ StDev & 0.479 & 0.476 & 0.462 & 0.474 & 0.431 & 0.479 \\ Variance & 0.229 & 0.227 & 0.214 & 0.225 & 0.186 & 0.229 \\ Minimum & 0.000 & 0.000 & 0.000 & \(-0.093\) & 0.000 & 0.000 \\ The 1 st Quartile & 0.070 & 0.073 & 0.076 & 0.074 & 0.070 & 0.067 \\ The 2 nd Quartile & 0.210 & 0.204 & 0.206 & 0.204 & 0.340 & 0.210 \\ The 3 rd Quartile & 0.500 & 0.504 & 0.501 & 0.500 & 0.636 & 0.505 \\ Maximum & 2.640 & 2.605 & 2.586 & 2.598 & 3.361 & 2.630 \\ Skewness & 2.080 & 2.100 & 2.060 & 2.100 & 0.820 & 2.090 \\ Kurtosis & 4.500 & 4.540 & 4.400 & 4.480 & 1.320 & 4.510 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics of the estimated and actual data selected for the testing phase. Compared to other algorithms, although kNN and Tree algorithms give satisfactory results, they need to catch up to RF and GB, especially regarding MSE, RMSE, and MAE. However, R\({}^{2}\) values of these algorithms (kNN: 0.971, Tree: 0.980) still indicate a strong explanatory power.