MACHINE LEARNING BASED SCHOLARSHIP AND CREDIT PRE-ASSESSMENT SYSTEM
Journal Title: Engineering and Technology Journal - Year 2022, Vol 7, Issue 1
Abstract
The researcher explained the implementation process of finding the scholarship for the students by using machine learning supervised learning algorithm i.e. Naïve Bayes algorithm. Addition to this it includes a small description of naïve bayes classifier which used to be used through the authors. It explains the significance of training facts set and trying out information set in Machine mastering techniques. Machine learning nowadays becomes plenty used technique in the field of IT industry. It is a very effective instrument and technique for many quite a number fields such as education, IT and even in enterprise industry. In this paper, the researcher attempt to find computerized end result reputation of scholarships of college students by way of using naïve bayes classifier algorithm primarily based on the scholar educational performance, conversation skills, greedy power, IHS, income, time management, regularity etc. A scholarship offers a strength and self assurance to a student. It also boosts the performance of students indirectly. Usually scholarships are furnished by governments or authorities organizations. It is very essential for students to recognize their personal potentiality early in their educational profession so that they faster its growth, receiving attention from an employer or corporation helps college students take this step. Students can apply for scholarships primarily based on the eligibility criteria (such as caste category, annual income, etc). The scholarship will be issued based on merit, student performance and career specific. Different schemes of scholarships are provided for the students based on distinct eligibility criteria. By the use of a naïve bayes classifier, the researcher acquired a end result with accuracy of 96.7% and error of 3.3%. The repute of scholarship students was once displayed in the form of yes or no
Authors and Affiliations
Serpil Erden , Kaveh Dehghanian
Keywords
Related Articles
- EP ID EP705174
- DOI 10.47191/etj/v7i1.03
- Views 104
- Downloads 0
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