## 4 Results and Discussion  
Consequently, the selection of an appropriate and suitable field of study is a paramount issue for both students and educational institutes. Therefore, in this section, we investigated the student field (student major) of specialization selection using different ML techniques based on student academic history and the current job market. We also visualized the current dataset to further understand the input variables and performed several experiments using Python to answer the research questions. We performed data visualization using Python to further understand the experimental dataset. These visualization results show the importance of input attributes in predicting field specialization. Figure 2 shows that higher secondary students who obtained jobs mostly majored in commerce and science. This result further indicates that commerce and science fields are currently in the greatest demand. Figure 3 shows that commerce and management students in higher secondary schools mostly take science and technology fields as postgraduates. In Figure 4, the blue portion (1) represents Mkt and finance, and the yellow portion (0) represents the Mkt and HR. Figure 4 indicates that 55.81% of students take the MKT& Finance program in postgraduate education, and others take the MKT & HR field. Figure 2: The proportion of different student fields of specialization in higher secondary school. At this step, we have obtained considerable knowledge about the data and can easily build models. Although there are lots of studies related to the selection of majors by students and its influential factors [1; 2; 3; 4; 5; 68], studies with a machine learning approach in this domain are limited [7]. In this study, we investigate the student study field based on student job markets, student academic history, and job experience. As the number of factors, size of the datasets, methods applied varied in different studies, it is impractical to compare these studies. Some of these studies were listed in Table 2. To find an appropriate ML model and student factors for our proposed recommendation system, this study conducts two experiments on datasets using the Python Sklearn library. Sklearn is a Python library that is used to train and deploy ML models. In this section, we investigate the following research question:  
Figure 4: The proportion of different student fields of specialization in postgraduate education. Figure 3: The proportion of different student fields of specialization in degree. **Question 1:** Can we model the student undergraduate major path choice according to the job market and student academic history by applying different ML algorithms, and if so, which ML classifier offers optimal performance in predicting student undergraduate major selection? The first experiment was conducted to explore this question. In the first experiment, we applied several Tree based ML models (DT algorithm, RF algorithm, extra tree classifier, and XGBoost) and SVM to our dataset by using their default parameters. Decision-tree has been widely implemented in various domains, such as in medical fields [75], marketing prediction tasks [76], and education [77; 78], due to its various well-known attractive features [79]. Features such as simplicity, comprehensive calculations, no required parameters, and the capability of handling mixed types of data, encouraged us to select DT in this study. Random forests are used in this study due to being easy and stable with many interesting properties. One of these interesting properties is that they provide a powerful computation of variables [80]. The extra tree classifier is one of the learning algorithms that can aggregate the results of multiple de-correlated decision trees collected in a \"forest\" to output its classification result. It has been applied in this study because it is similar to RF; however, it is faster, and its method in the construction of the decision tree in the forest is optimal.