## 1 Introduction
### Innovation of the Current Study  
The current study investigated the student field of specialization by using students' previous histories and job market information utilizing different ML techniques. ML approaches have been employed to accurately forecast college selection and select the best fit student major by means of common classification algorithms with diverse feature sets [1]. To achieve our current study goal, we trained different common ML models (extra tree classifiers [ETC], random forest [RF] classifiers, decision tree [DT] classifiers, gradient boosting classifiers [GBC], and support vector machine [SVM]) based on the current job market and students' previous histories. The results showed that RF and GBC predicted student majors with higher accuracy, as compared to DT, SVM, and ETC. Based on the Spearman correlation method, the study concluded that higher marks in higher secondary levels, entry tests, and universities, are good criteria for suggesting student field specialization. Furthermore, student work experiences and job placements are additional factors that are strongly related to student field specializations. In addition, these ML models and features 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 fields of specialization. Finally, this paper differs from other research in this area of predicting student field specializations because it is based on the job market and student histories and experiences.  
The current study investigated the following research questions.  
**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?  
**Question 2:** How is a student's undergraduate major path choice associated with that student's previous academic performance and the job market?  
**Contributions:** This study possesses contributions as enlisted below in the domain of selection of majors by students.  
1. The research utilized Kaggle repositories to devise a ML approach in selecting the field of specialization by students for future endeavors.
2. Several supervised learning techniques with 10-k fold cross-validation were utilized and yielded that RF, SVM, and GBC were the suitable classifiers for predicting student undergraduate major.
3. The influential factors related to selecting the right undergraduate major were also showcased. According to my knowledge, no work has been done to find student influential factors.
4. The findings may be integrated into the intelligent field recommender system for predicting suitable fields for students according to the job market.  
The rest of this paper is organized as follows. The literature review of the student study field is discussed in Section 2. The research materials and methods are discussed in Section 3, which contains all of the details of our proposed framework of the student study field selection system. Section 4 describes the experiment and discusses the results of the current study, where the performances of different ML algorithms are tested on the current study dataset. Finally, Section 5 presents the conclusions and future work of the current study.