Analysis of Income on the Basis of Occupation using Data Mining

Rehman, A U, Saleem, R M, Shafi, Z, Imran, M, Pradhan, M R and Alzoubi, H M (2022) Analysis of Income on the Basis of Occupation using Data Mining. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 16-17 Feb. 2022, Dubai, United Arab Emirates.

Full text not available from this repository. (Request a copy)

Abstract

Income forecasting is always the idea of knowing how many people us are around. Here we saw how to use machine learning technology to test people’s income according to their different lifestyles. The huge gap between wealth and income, especially in the United States, is a serious problem. One of the possible reasons for reducing the growing economic inequality in the world is the possibility of reducing poverty. The rule of worldwide ethical balance ensures maintainable improvement and financial steadiness of the nation. The governments of different countries are doing their best to solve this problem and find the best solutions. The point of this think about is to demonstrate the utilize of machine learning and Data mining strategies to fathom pay uniformity issues. Machine learning is classified as predicting whether a person has an annual income.

Affiliation: Skyline University College
SUC Author(s): Pradhan, M R ORCID: https://orcid.org/0000-0002-0115-2722 and Alzoubi, H M ORCID: https://orcid.org/0000-0003-3178-4007
All Author(s): Rehman, A U, Saleem, R M, Shafi, Z, Imran, M, Pradhan, M R and Alzoubi, H M
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: machine learning,maintainable, demonstrate,Classified
Subjects: B Information Technology > BL Machine Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 25 May 2022 08:25
Last Modified: 25 May 2022 08:25
URI: https://research.skylineuniversity.ac.ae/id/eprint/225
Publisher URL: https://doi.org/10.1109/ICBATS54253.2022.9759040
Publisher OA policy:
Related URLs:

Actions (login required)

View Item
View Item
Statistics for SkyRep ePrint 225 Statistics for this ePrint Item