Skip to main navigation menu Skip to main content Skip to site footer

Salary Prediction for Computer Engineering Positions in India

Abstract

Over the subsequent 20 years, India's economy has seen growth in many areas since the 1990s. Information technology is one of the industries that has grown significantly recently. Bharat's transformation from a sluggish economy to one of the top exporters of information technology services has been largely attributed to the information technology sector. Since there was such a huge need for skilled workers in the labor markets as a result of this boom, engineering has consistently ranked among the top high school courses of study. Additionally, engineering is a popular course of study due to the income potential and opportunity to progress technology. The primary compensation factors for recent engineering graduates in the Bharat Labor Markets are the focus of this study. The investigation looked at how factors like as demography, academic success, personality traits, and test scores affected starting pay. The analysis' findings showed that the significant predictors of starting pay were academic success at the faculty level, faculty name, college affiliation, and engineering major. The results also revealed that psychological characteristic skills, such as English and quantitative aptitude, as well as a desire to strive and complete a task well, are significant contributors to the starting pay of engineering graduates in Indian Labor Markets. This study used a machine learning method to carry out regression analysis. These procedures used the Naive Bayes, Random Forest, and Support Vector Machine algorithms (SVM).

 

Keywords

Salary prediction, Support Vector Machine, SVM, Naïve Bayes, Random Forest

PDF

Author Biography

Ashty Kamal Mohamed Saeed

 

 

Pavel Younus Abdullah

 

 

Avin Tariq Tahir

 

 


References

  1. S. Ang, L. Van Dyne, and T. M. Begley, The Employment Relationships of Foreign Workers versus Local Employees: A Field Study of Organizational Justice, Job Satisfaction, Performance, and OCB, J. Organ. Behav., vol. 24, no. 5, pp. 561583, 2003.
  2. (14) The Career DevelopmenT QuarTerly Cultural Trailblazers: Exploring the Career Development of Latina First-Generation College Students | Jeffrey mostade - Academia.edu. https://www.academia.edu/35008658/The_Career_DevelopmenT_QuarTerly_Cultural_Trailblazers_Exploring_the_Career_Development_of_Latina_First_Generation_College_Students?auto=download (accessed Dec. 14, 2022).
  3. B. D. Steffy, K. N. Shaw, and A. W. Noe, Antecedents and consequences of job search behaviors, J. Vocat. Behav., vol. 35, no. 3, pp. 254269, Dec. 1989, doi: 10.1016/0001-8791(89)90029-8.
  4. J. E. Rosenbaum, Tournament Mobility: Career Patterns in a Corporation, Adm. Sci. Q., vol. 24, no. 2, p. 220, Jun. 1979, doi: 10.2307/2392495.
  5. C. Ge, A. Kankanhalli, and K.-W. Huang, Investigating the Determinants of Starting Salary of IT Graduates, ACM SIGMIS Database DATABASE Adv. Inf. Syst., vol. 46, no. 4, pp. 925, Nov. 2015, doi: 10.1145/2843824.2843826.
  6. A. Carnevale, Workplace Basics: The Skills Employers Want., Train. Dev. J., 1988, Accessed: Dec. 14, 2022. [Online]. Available: https://www.semanticscholar.org/paper/Workplace-Basics%3A-The-Skills-Employers-Want.-Carnevale/c34f6b644ab0ff54a03a30096f0b0722c3d4b3ec
  7. H. B. Sagen, J. W. Dallam, and J. R. Laverty, Job Search Techniques as Employment Channels: Differential Effects on the Initial Employment Success of College Graduates, Career Dev. Q., vol. 48, no. 1, pp. 7485, 1999, doi: 10.1002/j.2161-0045.1999.tb00276.x.
  8. M. Boissiere, J. B. Knight, and R. H. Sabot, Earnings, Schooling, Ability, and Cognitive Skills, Am. Econ. Rev., vol. 75, no. 5, pp. 10161030, 1985.
  9. E. B. Jones and J. D. Jackson, College Grades and Labor Market Rewards, J. Hum. Resour., vol. 25, no. 2, pp. 253266, 1990, doi: 10.2307/145756.
  10. E. James, N. Alsalam, J. C. Conaty, and D. L. To, College quality and future earnings: where should you send your child to college?, Am. Econ. Rev., 1989, Accessed: Dec. 14, 2022. [Online]. Available: https://scholar.google.com/scholar_lookup?title=College+quality+and+future+earnings%3A+where+should+you+send+your+child+to+college%3F&author=James%2C+E.&publication_year=1989
  11. B. A. Weisbrod and P. Karpoff, Monetary Returns to College Education, Student Ability, and College Quality, Rev. Econ. Stat., vol. 50, pp. 491497, 1968.
  12. R. J. Murnane, J. B. Willett, and F. Levy, The Growing Importance of Cognitive Skills in Wage Determination. National Bureau of Economic Research, Mar. 1995. doi: 10.3386/w5076.
  13. G. Tchibozo, ExtraCurricular Activity and the Transition from Higher Education to Work: A Survey of Graduates in the United Kingdom, High. Educ. Q., vol. 61, pp. 3756, Jun. 2008, doi: 10.1111/j.1468-2273.2006.00337.x.
  14. S. Athey, L. Katz, A. Krueger, S. Levitt, and J. Poterba, What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes, Am. Econ. Rev. Pap. Proc., vol. 97, no. 2, 2007.
  15. J. Godofsky, C. Zukin, and C. V. Horn, Unfulfilled Expectations: Recent College Graduates Struggle in a Troubled Economy, Unfulfilled Expect..
  16. J. Gault, J. Redington, and T. Schlager, Undergraduate Business Internships and Career Success: Are They Related?, J. Mark. Educ., vol. 22, no. 1, pp. 4553, Apr. 2000, doi: 10.1177/0273475300221006.
  17. G. Callanan and C. Benzing, Assessing the role of internships in the careeroriented employment of graduating college students, Educ. Train., vol. 46, no. 2, pp. 8289, Jan. 2004, doi: 10.1108/00400910410525261.
  18. R. Fuller and R. Schoenberger, The Gender Salary Gap: Do Academic Achievement, Internship Experience, and College Major Make a Difference?., Soc. Sci. Q., 1991, Accessed: Dec. 14, 2022. [Online]. Available: https://www.semanticscholar.org/paper/The-Gender-Salary-Gap%3A-Do-Academic-Achievement%2C-and-Fuller-Schoenberger/5df50e9416b6911e01f2d92c88202e03028462a0
  19. P. Arcidiacono, Ability sorting and the returns to college major, J. Econom., vol. 121, no. 12, pp. 343375, Jul. 2004, doi: 10.1016/j.jeconom.2003.10.010.
  20. T. N. Daymont and P. J. Andrisani, Job Preferences, College Major, and the Gender Gap in Earnings, J. Hum. Resour., vol. 19, no. 3, pp. 408428, 1984, doi: 10.2307/145880.
  21. J. Grogger and E. Eide, Changes in College Skills and the Rise in the College Wage Premium, J. Hum. Resour., vol. 30, no. 2, pp. 280310, 1995, doi: 10.2307/146120.
  22. L. D. Loury, The Gender Earnings Gap among College-Educated Workers, Ind. Labor Relat. Rev., vol. 50, no. 4, pp. 580593, 1997, doi: 10.2307/2525263.
  23. D. Scholz, Risk Associated With Different College Majors, Park Place Econ., 1996, Accessed: Dec. 14, 2022. [Online]. Available: https://www.semanticscholar.org/paper/Risk-Associated-With-Different-College-Majors-Scholz/cf261a12b6dd272af0a66b792cf4da615ffb5e04
  24. G. Chia and P. W. Miller, Tertiary Performance, Field of Study and Graduate Starting Salaries, Aust. Econ. Rev., vol. 41, no. 1, pp. 1531, Mar. 2008, doi: 10.1111/j.1467-8462.2008.00471.x.
  25. M. Tan and M. Igbaria, Turnover and remuneration of information technology professionals in Singapore, Inf. Manage., vol. 26, no. 4, pp. 219229, Apr. 1994, doi: 10.1016/0378-7206(94)90094-9.
  26. J. J. Baroudi and G. E. Truman, Gender Differences in the Information Systems Managerial Ranks: An Assessment of Discriminatory Practices. Rochester, NY, Jan. 01, 1992. Accessed: Dec. 14, 2022. [Online]. Available: https://papers.ssrn.com/abstract=1289055
  27. V. K. Gokuladas, Technical and Non-Technical Education and the Employability of Engineering Graduates: An Indian Case Study. Rochester, NY, May 17, 2010. doi: 10.1111/j.1468-2419.2010.00346.x.
  28. V. Aggarwal, S. Srikant, and H. Nisar, AMEO 2015: A dataset comprising AMCAT test scores, biodata details and employment outcomes of job seekers, Proc. 3rd IKDD Conf. Data Sci. 2016, pp. 12, Mar. 2016, doi: 10.1145/2888451.2892037.
  29. What is regression in machine learning? - Quora. https://www.quora.com/What-is-regression-in-machine-learning (accessed Dec. 14, 2022).
  30. P. Pedamkar, Naive Bayes Algorithm | Discover the Naive Bayes Algorithm, EDUCBA, May 14, 2019. https://www.educba.com/naive-bayes-algorithm/ (accessed Dec. 14, 2022).
  31. Random Forest Algorithm - How It Works and Why It Is So Effective. https://www.turing.com/kb/random-forest-algorithm (accessed Dec. 14, 2022).
  32. SVM | Support Vector Machine Algorithm in Machine Learning. https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/ (accessed Dec. 14, 2022).
  33. J. Brownlee, A Gentle Introduction to k-fold Cross-Validation, MachineLearningMastery.com, May 22, 2018. https://machinelearningmastery.com/k-fold-cross-validation/ (accessed Dec. 14, 2022).

Downloads

Download data is not yet available.

Similar Articles

11-17 of 17

You may also start an advanced similarity search for this article.