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An Automated Visa Prediction Technique for Higher Studies Using Machine Learning in the Context of Bangladesh
Asif Ahmmed, Md. Jueal Mia , Md. Tipu Sultan, Sk. Hasibul Islam Shad, Sourov Mazumder,

Nowadays in Bangladesh, one of the most common scenarios for our students is moving to other countries for higher studies. In order to succeed, students need to pick the right direction before applying for a higher education visa. This article aims to implement which student’s visa will get approved or rejected for their higher studies abroad by using machine learning. Throughout this analysis, we predict the visa for higher studies based on student data. Then we process the data (such as cleaning, transformation, integration, standardization, and feature selection). We used multiple classification methods later on, i.e., C4.5 (j48), k-NN, naive Bayes, random forest, SVM, neural network, for classifying these models. Depending on the outcome of the study, it was observed that the accuracy, confusion matrix, and other factors of a random forest classifier are more compatible than others. GRE score and undergraduate CGPA, are two of the most significant attributes that rely on deciding the success of visa acceptance for higher studies also discovered.

Higher studies Machine learning Classification techniques Confusion matrix Random Forest
Journal or Conference Name
Lecture Notes in Networks and Systems
Publication Year