Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance

Autor: Laxmi Shanker Maurya, Md Shadab Hussain, Sarita Singh
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 35, Iss 6, Pp 403-420 (2021)
Druh dokumentu: article
ISSN: 0883-9514
1087-6545
08839514
DOI: 10.1080/08839514.2021.1901032
Popis: In the era of globalization, student placement is very challenging issue for all educational institutions. For engineering institutions, placement is a key factor to maintain good ranking in the university as well as in other national and international ranking agencies. In this paper, we have proposed a few supervised machine learning classifiers which may be used to predict the placement of a student in the IT industry based on their academic performance in class Tenth, Twelve, Graduation, and Backlog till date in Graduation. We also compare the results of different proposed classifiers. Various parameters used to compare and analyze the results of different developed classifiers are accuracy score, percentage accuracy score, confusion matrix, heatmap, and classification report. Classification report generated by developed classifiers consists of parameters precision, recall, f1-score, and support. The classification algorithms Support Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Stochastic Gradient Descent, Logistic Regression, and Neural Network are used to develop the classifiers. All the developed classifiers are also tested on new data which are excluded from the dataset used in the experiment.
Databáze: Directory of Open Access Journals
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