Authors :
Sanskruti Naik; Ganesh Manerkar
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3khDqCt
DOI :
https://doi.org/10.5281/zenodo.6496523
Abstract :
Education loans help students to cover the cost
of tuition, books and supplies, and living expenses while
in the process of pursuing a degree. Education loans are
granted by private banks and by government
organizations. This paper isan analysis on student loan
data for the interest free education loans granted to
students as per the standards and rules of Goa
Education Development Corporation(GEDC). The
dataset is prepared complying the standards of criteria
mentionedby organization. The accuracy of prediction is
compared using models like Support Vector
Machine(SVM), Random forest(RF), Logistic
regression(LR), Decision tree classifier and XG-boost.
Keywords :
Loan, Prediction, Support Vector Machine, RanDom Forest, Logistic Regression, Decision Tree Classifier, XG- Boost.
Education loans help students to cover the cost
of tuition, books and supplies, and living expenses while
in the process of pursuing a degree. Education loans are
granted by private banks and by government
organizations. This paper isan analysis on student loan
data for the interest free education loans granted to
students as per the standards and rules of Goa
Education Development Corporation(GEDC). The
dataset is prepared complying the standards of criteria
mentionedby organization. The accuracy of prediction is
compared using models like Support Vector
Machine(SVM), Random forest(RF), Logistic
regression(LR), Decision tree classifier and XG-boost.
Keywords :
Loan, Prediction, Support Vector Machine, RanDom Forest, Logistic Regression, Decision Tree Classifier, XG- Boost.
