## 3 Results
LR exhibits a significantly lower performance than other algorithms; it remained weak in prediction accuracy with the highest MSE (0.044) and RMSE (0.210) values. The R\({}^{2}\) value of LR is also relatively low, at 0.808, compared to other algorithms, which shows that it has limited success in explaining the variance of the dependent variable. These results reveal that RF and GB are more reliable and effective algorithms in prediction tasks. For the reliability of the ANOVA test performed for the prediction data of the dependent variable from machine learning algorithms, \(S\), R\({}^{2}\), and R\({}^{2}\) (adjusted) data were calculated and shared in Table 8. The results show that all algorithms successfully explain the variance of the dependent variable. R\({}^{2}\) values are at the highest level with 89.30% for the GB model, followed by RF (88.83%), kNN (88.81%), and Tree (88.36%) models, respectively. This situation reveals that the GB model is one step ahead of other algorithms in explaining the variability of the dependent variable. However, there is a general consistency between _Po_ and the algorithms regarding R\({}^{2}\) values. When the standard error (S) values are examined, it is observed that the LR model has the lowest value with 0.054663, and therefore, the error level in the estimates is much lower compared to the other models. However, the LR model's R\({}^{2}\) (98.46%) and R\({}^{2}\) adjusted (98.09%) values indicate high performance. However, this situation suggests that the model may tend to overfit. The RF, kNN, and GB models have similar standard error values (0.177563, 0.172650, and 0.173173, respectively), and there is no significant difference between the performances of these models. The tree model also produced results compatible with the other models, with a S value of 0.182332. In general, while the GB and RF models provide balanced and reliable results in estimate accuracy, the LR model should be carefully evaluated against possible overfitting despite its high performance. The results of the ANOVA test performed for the estimation data of the dependent variable from the machine learning algorithms are given in Table 9. The effects of the \begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline GB & 0.003 & 0.058 & 0.040 & 0.985 \\ kNN & 0.007 & 0.082 & 0.051 & 0.971 \\ RF & 0.003 & 0.053 & 0.036 & 0.988 \\ Tree & 0.004 & 0.067 & 0.045 & 0.980 \\ LR & 0.044 & 0.210 & 0.148 & 0.808 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance measurement data of machine learning algorithms. \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & \(\mathbf{S}\) & **R\({}^{2}\)** & **R\({}^{2}\) (Adjusted)** \\ \hline PO & 0.183188 & 88.25\% & 85.36\% \\ RF & 0.177563 & 88.83\% & 86.08\% \\ KNN & 0.172650 & 88.81\% & 86.06\% \\ GB & 0.173173 & 89.30\% & 86.67\% \\ LR & 0.054663 & 98.46\% & 98.09\% \\ Tree & 0.182332 & 88.36\% & 85.50\% \\ \hline \end{tabular}
\end{table} 
Table 8: S, R\({}^{2}\), and R\({}^{2}\) (adjusted) of ANOVA analysis of actual and estimated data selected for the test phase. independent variables on the prediction data of the dependent variable were statistically analyzed. The _p_-values of the ANOVA test evaluating the impact of the independent variables on the actual and predicted data of the dependent variable are presented. The results show that aFll independent variables generally have a statistically significant effect (_p_ < 0.05). Among the prediction data of the _Po_ and machine learning algorithms, the _Ws_ variable strongly impacted all models (_p_ \(\leq\)_0.002_). The _Wd_ and _Nd_ variables also generally produced significant results, but relatively higher _p_-values were observed for _Nd_ in the kNN (_p_ = 0.006) and Tree (_p_ = 0.021) models. On the other hand, the _Rs_ and _Wf_ variables also have a significant effect on the prediction data. However, for the LR model, the _Rs_ variable (_p_ = 0.030) and the _Wt_ variable (_p_ = 0.020) produced values closer to the significance limit.