Computational Statistics and Machine Learning Techniques for Effective Decision Making on Student’s Employment for Real-Time

Deepak Kumar, Chaman Verma, Pradeep Kumar Singh, Maria Simona Raboaca, Raluca-Andreea Felseghi and Kayhan Zrar Ghafoor
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Deepak Kumar: Department of Computer Science and Applications, Guru Kashi University, Bathinda 151302, Punjab, India
Chaman Verma: Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary
Pradeep Kumar Singh: Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad 201009, Uttar Pradesh, India
Maria Simona Raboaca: ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
Raluca-Andreea Felseghi: Faculty of Electrical Engineering and Computer Science, “Stefan cel Mare” University of Suceava, 720229 Suceava, Romania
Kayhan Zrar Ghafoor: Department of Computer Science, Knowledge University, Erbil 44001, Iraq

Mathematics, 2021, vol. 9, issue 11, 1-29

Abstract: The present study accentuated a hybrid approach to evaluate the impact, association and discrepancies of demographic characteristics on a student’s job placement. The present study extracted several significant academic features that determine the Master of Business Administration (MBA) student placement and confirm the placed gender. This paper recommended a novel futuristic roadmap for students, parents, guardians, institutions, and companies to benefit at a certain level. Out of seven experiments, the first five experiments were conducted with deep statistical computations, and the last two experiments were performed with supervised machine learning approaches. On the one hand, the Support Vector Machine (SVM) outperformed others with the uppermost accuracy of 90% to predict the employment status. On the other hand, the Random Forest (RF) attained a maximum accuracy of 88% to recognize the gender of placed students. Further, several significant features are also recommended to identify the placement of gender and placement status. A statistical t -test at 0.05 significance level proved that the student’s gender did not influence their offered salary during job placement and MBA specializations Marketing and Finance (Mkt&Fin) and Marketing and Human Resource (Mkt&HR) ( p > 0.05). Additionally, the result of the t -test also showed that gender did not affect student’s placement test percentage scores ( p > 0.05) and degree streams such as Science and Technology (Sci&Tech), Commerce and Management (Comm&Mgmt). Others did not affect the offered salary ( p > 0.05). Further, the ? 2 test revealed a significant association between a student’s course specialization and student’s placement status ( p < 0.05). It also proved that there is no significant association between a student’s degree and placement status ( p > 0.05). The current study recommended automatic placement prediction with demographic impact identification for the higher educational universities and institutions that will help human communities (students, teachers, parents, institutions) to prepare for the future accordingly.

Keywords: association; classification; machine learning; placement status; placement gender (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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