## 3 Results
### The Results of Performance Evaluation Metrics  
In e-scooter vehicle use, the test data used in ML algorithms to estimate the package or mail delivery times and the distribution energy cost to the administration are shown in Figure 5, being random. RF, GB, \(k\)NN, and NN algorithms developed for this study were tested in both phases of the ML approach to analyse prediction data using actual data. The performance criteria data of the algorithms were calculated to verify the validity of the predictive data obtained from the ML algorithms. Performance measurement values of ML algorithms are generally considered at four different levels. The performance criteria values of the data used in the training phase and the results of the data used in the test phase are shared in Table 6. The standard deviation (StDev) data of the train and test phases of the data in the data sets used by ML algorithms were also calculated. Figure 5: The testing data of (**a**) the delivery time output variable and (**b**) energy cost output variable. The RF algorithm shows the best performance according to the performance measurement metrics for the time output factor in the testing phase of ML algorithms. This algorithm's MSE, RMSE, MAE, and R\({}^{2}\) values were calculated as 44.13, 6.64, 3.08, and 0.95, respectively. These are the smallest values for error margins and the largest for the correlation coefficient among the values of other algorithms. Likewise, if the same output parameter is in the training phase, the RF algorithm provides the best result compared to different algorithms. MSE, RMSE, MAE, and R\({}^{2}\) values of the RF algorithm performance results of the time-factored training phase were calculated as 37.8510, 6.1520, 3.077, and 0.960, respectively. Standard deviation data of test and train data sets obtained by ML models were computed. For the train data set, the standard deviation data of the RF, GB, \(k\)NN, and NN algorithms were calculated as 29.15, 29.34, 28.53, and 28.87, respectively. This standard deviation data is realized for the time output parameter used in ML algorithms. For the test data set of the same output variable, the standard deviation data of the RF, GB, \(k\)NN, and NN algorithms were computed as 29.05, 28.96, 28.62, and 29.22, respectively. Among the ML algorithms, the \(k\)NN model has the lowest standard deviation value for both time and energy costs. The standard deviation data of the test and train data sets obtained by the ML models of the energy cost, which is the other output parameter of this study, were compared. For the train data set, the standard deviation data of the RF, \(k\)NN, NN, and GB algorithms were calculated as 0.10, 0.11, 0.08, and 0.11, respectively. For the energy output variable test data set, the standard deviation data of the RF, KNN, NN, and GB algorithms were calculated as 0.10, 0.10, 0.8, and 0.11, respectively. Among the ML algorithms, the \(k\)NN model has the lowest standard deviation value for both time and energy costs. Energy cost draws attention to the choice of e-scooter vehicles as a package or mail distribution vehicle. For the energy factor, the GB algorithm provided the best performance according to the performance measurement metrics in the testing phase of the ML algorithms. This algorithm's MSE, RMSE, MAE, and R\({}^{2}\) values were calculated as 0.001, 0.020, 0.010, and 0.960, respectively. These are the smallest values for error margins and the largest for the correlation coefficient among the values of other algorithms. Likewise, in the training phase of the same output parameter, the GB algorithm shows the best result compared to different algorithms. MSE, RMSE, MAE, and R\({}^{2}\) values of the GB algorithm performance results of the energy factor training phase were calculated as 0.001, 0.023, 0.011, and 0.954, respectively. The NN model shows the worst mark among the ML algorithms operated when considering the energy cost.