## 4 Data Mining Analysis and Experimental Simulation
### Machine Learning Models  
This study focused on the admission procedures of universities, considering additional features such as high school course grades. This research employed three machine learning models to analyze the suggested acceptance procedures. This study was conducted using Windows 10, and Python was the programming language. It utilized a 2.90 GHz Intel Core (TM) i7 CPU and 4 GB of RAM for processing. Each test was conducted five times, and the average results are reported.  
This section of the study aimed to enhance the university admission procedures by testing the dataset and features. This research evaluated the dataset using three machine learning models, which were KNN, DT, and SVM. The parameters for these models were set to their default values. The dataset used for this study comprised 19 features, which included high school courses, GAT, and AT. The classifier generated four classes that corresponded to specific domains, as listed in Table 3.  
During the KNN processing, an accuracy rate of 100% was achieved when K = 1. However, increasing the value of K resulted in a reduction in accuracy. For instance, when  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**College** & **GPAU—GPAH** & **GPAU—GAT** & **GPAU—AT** & **GPAU—QS** \\ \hline Medicine & 0.33 & 0.16 & 0.20 & 0.26 \\ Dentistry & 0.28 & \(-\)0.06 & 0.21 & 0.16 \\ Nursing & 0.45 & 0.13 & 0.23 & 0.31 \\ Pharmacy & 0.40 & 0.10 & 0.20 & 0.30 \\ Applied Medical Sciences & 0.42 & 0.21 & 0.41 & 0.45 \\ Public Health & 0.38 & 0.28 & 0.39 & 0.46 \\ CSIT & 0.50 & 0.47 & 0.52 & 0.61 \\ Design & 0.53 & 0.33 & 0.44 & 0.53 \\ Engineering & 0.50 & 0.43 & 0.50 & 0.57 \\ Science & 0.52 & 0.36 & 0.53 & 0.58 \\ Business & 0.36 & 0.21 & 0.43 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Illustrates the correlation between GPAU and GPAH, GAT, AT, and QS for scientific colleges.  
K = 3, the accuracy of the KNN model was 91%, as demonstrated in Figure 2. In Figure 2, the data points represent examples from the dataset, while the background areas indicate the four classes, with class 1 represented by white, class 2 represented by yellow, class 3 represented by pink, and class 4 represented by gray. The figure demonstrates that the majority of the points were classified accurately in the intended classes.  
Figure 3 shows the result of the DT, where the accuracy rate reached 81%. It uses the same class backgrounds as Figure 2. Obviously, some points were classified on the borders between the classes, and one of the points was misclassified. Furthermore, when the SVM classifier was applied to the dataset, an accuracy rate of 75% was achieved, as illustrated in Figure 4, which also uses the same class backgrounds as Figure 2. The figure reveals that the SVM struggled with classification due to the narrow margins between classes. Additionally, a point was misclassified, and some points were located on the borders between the classes. Based on the results obtained by running the three classifiers, it was proven that the KNN machine learning model provided the highest accuracy rate and was the most suitable classifier for the proposed method.