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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 8 Issue 9 ijcatr08091009

IJCATR Volume 8 Issue 9

Engineering College Admission Preferences Based on Student Performance

Dhruvesh Kalathiya, Rashmi Padalkar, Rushabh Shah, Sachin Bhoite, Dr. Ajit More
10.7753/IJCATR0809.1009
keywords : Decision Tree, Random Forest, KNN, Random Forest, Extra Tree Classifier, SVC, Probabilities

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As we know that after the 12th board results, the main problem of a student is to find an appropriate college for their further education. It is a tough decision to make for many students as to which college they should apply to. We have built a system that compares the student’s data with the past admission data and suggests colleges in a sequence of their preference. We have used Decision Tree, Support Vector Classifier, Extra Tree Classifier, Naïve Bayes, KNN and Random Forest as our statistical model to predict the probability of getting admission to a college. It was observed that the performance of Random Forest was achieved highest among all.
@artical{d892019ijcatr08091009,
Title = "Engineering College Admission Preferences Based on Student Performance",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="379 - 384",
Year = "2019",
Authors ="Dhruvesh Kalathiya, Rashmi Padalkar, Rushabh Shah, Sachin Bhoite, Dr. Ajit More "}
  • This paper proposes a model that will be able to directly help students to predict a college without any need of middlemen.
  • The objective is to find an appropriate college using student’s profile.
  • The machine learning model is solving this using prior data of students.
  • We are able to achieve better performance by tuning our model keeping the base estimator as random forest.
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