## 3 Results and Discussion
The data is usually divided into a 60-70-80% train set and a 40-30-20% test set. There is no definite method for separating the data into training and testing. Here, the best ratio is determined by experimenting with the data set [34; 35]. In the study, the data set was divided into two at different rates. The training and test data were divided into two in the ratio of 70-30% because it gave the most successful results resulting from the experiments. The coefficient of determination (R\({}^{2}\)), Mean Squared Error (MSE), and Mean Absolute Error (MAE) metrics were calculated to evaluate the machine learning methods. The coefficient of determination (R\({}^{2}\)) shows the rate of explanation of independent variables. In other words, it represents the variance ratio of the dependent variable ex  
Figure 4: The steps of the study in stages. plained by the independent variables. The R\({}^{2}\) measure is in the range [0, 1]. A coefficient of determination of zero indicates that the independent variables cannot explain the dependent variable at all, while a coefficient of determination of one indicates that they can fully explain the dependent variable. Here, zero indicates that the model has zero explanatory power, and one indicates that the model has 100% explanatory power. According to the calculated R\({}^{2}\) value, it can be said at what percentage level the independent variables can explain the dependent variable [36]. This value should be close to 1. \[\text{R}^{2}=1-\frac{\text{Unexplained Variation}}{\text{Total Variation}} \tag{14}\]  
Mean squared error (MSE) is equal to the mean squared difference between predicted values and actual values. MSE is often used in regression models. Because MSE squares the error, major errors are clearly highlighted. MSE is a metric that ranges from 0 to infinity, and values close to zero are considered better. The lower the MSE value in the calculated MSE values for the same dataset, the more accurate the model is. [37]. \[\text{MSE}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}\text{e}_{\text{i}}^ {2} \tag{15}\]  
In the formula, n represents the number of data points, and e is the error value. The mean absolute error (MAE) is calculated by averaging the absolute values of the prediction errors. It is often preferred for determining the error values of the models because it can be easily interpreted. Since MAE is calculated by taking the absolute value of the errors, the direction of the errors is not important. MAE values can vary from 0 to \(\infty\). The lower the MAE value, the lower the error value of the model [38]. The MAE formula is shown in Equation (16):  
\[\text{MAE}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}\lvert\text{e}_{ \text{i}}\rvert \tag{16}\]  
Three different machine learning techniques were used in the study: artificial neural networks, logistic regression, and decision tree regression. In the artificial neural network method, a feedback model consisting of 11 input neurons and one output neuron was developed. As a result of trial-and-error methods, three hidden layers were used, which was the most successful result. There are three neurons in each hidden layer. The model is shown in Figure 5.  
Figure 5: ANN model used in the study. After testing various functions for the activation function, the sigmoid function was preferred because it revealed the most successful result. One of the parameters that affect the success of the model in artificial neural networks is learning. A feedback learning algorithm was used in the study. In this method, 100 iterations were performed. The maximum likelihood method was used to estimate the parameters for the logistic regression model. In the study, the log-likelihood value was \(-15.882\) following 100 iterations. In the decision tree regression used in the study, the Gini index was used as a quality measure, and minimum description length (MDL) was used as a pruning method. The developed models were run 10 times on randomly selected training and test data, and the average was calculated.