## 3 Materials and Methods
### Proposed Framework  
Undergraduate major selection is a crucial and challenging decision for the university and the student during the process of admission to fulfill their future success. Due to bad counseling by the admission office center in the university, students go into the wrong study field; as a result, student learning will be affected [15]. Every country faces problems with students selecting the right undergraduate major course, and past studies have indicated that institutions have seen a significant increase in the number of students enrolling as undeclared. In reality, it is estimated that over 50% of students enter college undecided and that approximately 75% of students change their majors at least once before they complete their degree [64; 65]. This study builds a student major intelligent system that provides feedback to students and universities about study field selection based on the data extracted from the university database. Finding and building an intelligent system model of students' field of specialization could be of high value in developing an intelligent system to easily recommend a specialization to potential applicants who are often unsure of their desired field of specialization. In the current study, we verified the performance of different supervised ML techniques to predict students' fields of specialization based on student history and the job market. High-performance ML algorithms can provide a high degree of support to student major intelligent systems. Based on performance, the student major intelligent system can provide various support to educational institutions on many issues, such as (1) helping the university administration staff in making quick decisions about students' fields of study; (2) recommending personalized study fields according to students preferences and the job market; (3) decreasing the workload for the admission office; (4) enrolling students according to their preferences and job markets; (5) identifying at-risk students and chances of success of students at early stages; and (6) intervening with the student at early stages so that course dropout rates will decrease, as well as to ensure that students go into the right study fields. Figure 1 shows the basic architecture of the intelligent system for student field of specialization recommendation. The proposed student field intelligent system framework has four major phases, which are shown below. **Step 1 Pre-processing**: In the first phase known as Phase-1 (preprocessing), raw information (216 student records) was collected from a university database, as shown in Table 1. Subsequently, we applied different preprocessing techniques by using the Python module, such as removing missing records, deleting irrelevant student records, normalization, outlier detection, and hot encoding. To increase the proposed system performance, we also created new features by creating different categories at different education levels (ssc_p_categ, hsc_p_categ, mba_p_categ, degree_p_categ and etest_p_categ). To remove the missing records, we used different missing record techniques. Sometimes, ML techniques do not process the categorical technique; therefore, we applied the hot encoding technique. Additionally, the cleaned data were normalized because the ML model does not work correctly on non-normalized data. Finally, the preprocessing module converted the data into an acceptable form for the ML models, and the data were ready for the next phase. The current study dataset contained 19 input features (previous exam history, salary, and experiences of students), and the explanation of our current study dataset is shown in Table 1. Specialization is the target variable (Y) that finds the class of independent variables. When students belong to Management and Human resource management (HR) study fields (Mk and HR) then target variable (Y) is set \"1\" and if students belong to Management and Finance study field then target variable (Y) is set to \"0\".