Predictive Insights: using Machine Learning to Determine Your Future Salary
M. Saraswathi1, J. Akhila2, K. Sireesha3
1Dr. M. Saraswathi, Assistant Professor, Department of Computer Science Engineering, Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Enathur (Tamil Nadu), India.
2J. Akhila, B.E, 4th Year Student, Department of Computer Science Engineering, Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Enathur (Tamil Nadu), India.
3K. Sireesha, B.E, 4th Year Student, Department of Computer Science Engineering, Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Enathur (Tamil Nadu), India.
Manuscript received on 25 March 2023 | Revised Manuscript received on 05 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 1-7 | Volume-13 Issue-2, May 2023 | Retrieval Number: 100.1/ijsce.B36050513223 | DOI: 10.35940/ijsce.B3605.0513223
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Knowing one’s expected salary can be a crucial consideration when deciding whether to change careers or seek higher education in today’s fiercely competitive work market. Accurate salary forecasts can give important information about the earning potential of various professions because there are so many students graduating each year and workers looking to switch sectors. In order to forecast a salary range, this paper suggests a computerized method that considers a person’s country, level of education, number of years of experience, and area of specialization. This kind of system has obvious benefits because it gives individuals and groups the power to decide wisely about job prospects, wage negotiations, and employee retention. The system’s data can be used by researchers, academic institutions, and policymakers to evaluate labor market trends and reach informed decisions. The reliability and correctness of the system’s data, the forecasting models employed, and the regularity of system maintenance and updates will all have an impact on these factors. However, it is a promising area for further research and development due to the benefits of having a reliable technique for estimating salaries.
Keywords: Machine learning, Prediction, Regression, Supervised learning.
Scope of the Article: Machine learning