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International Journal of Soft Computing & Engineering
Article . 2023 . Peer-reviewed
Data sources: Crossref
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Article . 2023
License: CC BY
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Predictive Insights: using Machine Learning to Determine Your Future Salary

Authors: Dr. M. Saraswathi; J. Akhila; K. Sireesha;

Predictive Insights: using Machine Learning to Determine Your Future Salary

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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