## 4 Results and Discussion
The tree-based algorithm is simple and requires less data; additionally, it is easy to see that the training data is more accurate than the training data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \
\begin{tabular}{c} **Year of** \\ **Publication** \\ \end{tabular} & **Country** & **No of Participants** &
\begin{tabular}{c} **Some of the Most Influential** \\ **Factors in Selecting Major** \\ \end{tabular} & **Methods Used** \\ \hline
[69] & 2006 & USA & 89 &
\begin{tabular}{c} personal interest in the subject \\ matter, long-term salary \\ prospects, probability of \\ working in the field after \\ graduation, starting salary, \\ and prestige of the profession \\ \end{tabular} & Average \\
[70] & 2017 & Korea & 816 & \
\begin{tabular}{c} collaborative learning; \\ technology-based learning; \\ self-regulated learning; \\ hands-on activities; belief in \\ major benefits \\ \end{tabular} &
\begin{tabular}{c} Cronbach’s Alpha, \\ exploratory factor \\ analysis (EFA), and \\ confirmatory factor \\ analysis (CFA) \\ \end{tabular} \\ \hline
[71] & 2016 & Korea & 195 & \
\begin{tabular}{c} Gender, Grade, \\ Acquaintance’s \\ recommendation, Daily hours \\ of study, Place of residence \\ \end{tabular} &
\begin{tabular}{c} [PERSON]’s exact test, \\ _t_-test, one way \\ ANOVA, Mann \\ Whitney test, and \\ ANCOVA \\ \end{tabular} \\ \hline
[14] & 2019 & Saudi Arabia & \begin{tabular}{c} 239 prospective \\ participants and \\ 392 university \\ participants \\ \end{tabular} & \
\begin{tabular}{c} the outcome of student’s \\ qualification exams and \\ overall high school grades \\ \end{tabular} &
\begin{tabular}{c} Fuzzy Expert \\ System \\ \end{tabular} \\ \hline
[72] & 2015 & Indonesia & 40 & \
\begin{tabular}{c} value of national examination, \\ the value of the placement \\ test, and value of School \\ Exams \\ \end{tabular} &
\begin{tabular}{c} Fuzzy Multiple \\ Attribute Decision \\ Making (FMADM) \\ \end{tabular} \\ \hline
[73] & 2011 & Iran & 465 & \
\begin{tabular}{c} Students’ interest and \\ decision \\ \end{tabular} &
\begin{tabular}{c} Structural \\ Equation Modeling \\ \end{tabular} \\ \hline
[74] & 2004 & USA & 114 & \
\begin{tabular}{c} Financial aid, previous \\ education, potential \\ career/degree characteristics, \\ and information sources. \\ \end{tabular} &
\begin{tabular}{c} chi-square and \\ analysis of \\ variance for mean \\ differences \\ \end{tabular} \\ \hline
[55] & 2020 & India & 550 & \
\begin{tabular}{c} Marks in Board exam, Family \\ income, Scholarship, etc. \\ \end{tabular} &
\begin{tabular}{c} SVM, k-nearest \\ neighbor and \\ Neural Networks \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Related studies are based on major influence factors for the recommendation of the future domain. to understand and easily implemented [81, 82]. Conversely, deep learning (artificial neural network) is complex, computationally expensive, and requires more data [83]. Additionally, we did not use the naive Bayes algorithm, which is a very commonly used algorithm to solve real-life problems because it overlooked how to calculate probabilities [84]. We used SVM because the performance is good using small datasets [85, 86]. Moreover, it does not apply a strict requirement on the number of samples and sample points; additionally, it can process error distributions and can be easily promoted. XGBoost was used in this study because it has higher predictive accuracy than other ML algorithms, such as SVM and DT [87, 88]. Our dataset contains both numeric and categorical attributes. Therefore, the selected model must perform well on categorical data. For the first experimental dataset, we used 19 input features, which are shown in Table 1, and the target variable was _specialization_. First, we converted the target variable (specialization) into a binary form (0,1) by using python, wherein \"0\" denotes marketing and finance (Mk&Fin) and \"1\" represents the Market and Human resources Field (Mk&HR).