## 4 Results and Discussion
The second experiment investigated how a student's field of specialization was associated with that student's previous academic history, salary, and experience. First, we observed the baseline characteristics of different selected variables, such as secondary education percentage, higher secondary education percentage, degree percentage, MBA percentage, and employability test percentage. Then, we performed statistical analysis (Spearman correlation) to assess the relationship between a student's major and other input variables. The Spearman correlation shows how closely two variables are related. Table 9 shows the Spearman correlation between student field specialization and other input variables of the  
Figure 5: ROC curves of all the classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **TN = 23 (0.66)** & **FP = 12 (0.34)** \\ \hline
**Actually (0)** & FN = 13 (0.43) & TP = 17 (0.57) \\ \hline
**Actually (1)** & **Predicted (0)** & **Predicted (1)** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix of the XGBoost classifier. current study. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play a significant role in student field of specialization, and we can easily suggest study fields according to these features. Furthermore, student work experience (work_exp) and job placement (status) also impact student field specialization. Several interesting observations are obtained from the above statistical analysis. First, students at the high secondary stage are very excited about their field in university (undergraduate level) or undergraduate major path choice. At this level, every student wants to go into a good study field. In other words, students place high importance on student field specialization decisions. Second, students who get admitted to their favorite field graduate with higher grades. Third, the student field of specialization also affects student work experience and market salary. Fourth, students who applied for their favorite field may receive high marks on their university entry test. Fifth, students who graduate in their favorite field have a high chance of getting a job. In addition, the student marks percentage in a higher secondary, university degree, and entry test is useful criteria for study field suggestion. The result also demonstrates that student marks percentage in higher secondary, university degree, entry test assignment, and other factors are beneficial to the intelligent recommendation system. Using these variables, the proposed recommender system correctly predicts student field specialization according to their marks and preferences. The current study results show that we can design a recommendation system for predicting the field of specialization using RF, GBC, and SVM classifiers. The proposed recommendation system will offer a variety of functions to students and college/university staff, such as recommending appropriate fields of study for students, ranking highly demanding fields in the coming and current years, and predicting the future salary of recommended fields. The results also show that higher secondary education is an appropriate stage to enter a good study field. Moreover, having a suitable specialization might affect students' academic performance and job salary, which could assist in lessening their