Personality Traits and Classification using Machine Learning

Authors

  • M. Nagavamsi Assistant Professor, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Vemula Sriya UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Bommidi China Babu UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Koka Divya UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Gajjala B.R.V.S Sailesh UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author

Keywords:

Naïve Bayes Algorithm, Automated Personality Classification, Classification, Data Mining, Support Vector Machine

Abstract

Personality is one feature that determines how people interact with the outside world. Personality can be defined as a necessary element of a person’s behavior. The way people interact with other people determines their personality. This project covers the topic of Automated Personality Classification [1] – a system that analyses the personality of a user based on certain features using Data Mining Algorithms. In this project, a system is proposed which analyses the personality of an applicant. This system will be helpful for organizations as well as other agencies who would be recruiting applicants based on their personality rather than their technical knowledge. The personality prediction results are based on Big Five Personality traits and the classification is done using Naïve Bayes Algorithm and Support Vector Machine [2].

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References

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Published

26-04-2024

Issue

Section

Research Articles

How to Cite

[1]
M. Nagavamsi, Vemula Sriya, Bommidi China Babu, Koka Divya, and Gajjala B.R.V.S Sailesh, “Personality Traits and Classification using Machine Learning”, Int J Sci Res Sci Eng Technol, vol. 11, no. 2, pp. 483–490, Apr. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://ijsrset.com/index.php/home/article/view/IJSRSET2411271

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