Abstract:
Computer science is arguably one of the most common fields across both Bangladesh and
the world today. It is obvious that a statistically significant percentage of learners struggle
to achieve the peak of this discipline due to the lack of skill in this discipline. Without a
doubt, one of the most popular studies is going abroad for higher studies. It is really
necessary for students to choose the correct path before applying for a higher education
visa in order to succeed. In this work, we predict the visa for higher studies based on
student’s information. Then we process those data (like; cleaning, transformation,
integration, standardization, feature selection). Later we used different classification
techniques i.e. C4.5 (j48), K-NN, Naive Bayes, Random Forest, SVM, Neural Network to
classify these profiles. Based on the result analysis, it has been found that accuracy and
other factors of a confusion matrix for Random Forest classifiers are more cogent than
others. We also find out the attributes upon which a student’s visa accepted depends
mostly. Therefore, the GRE score, Undergraduate CGPA, are two of the most important
factors to determine success in the visa approval for higher studies.
