Abhijeet Gupta, Shruti Ajay Singh, Aditya Shrivastava
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The career dataset introduces the question of how we can predict an individual’s career path in the future. And this can have a variety of application in the industry including enhancing human resources, career guidance, keeping track of future trends, et al. In this paper we propose a method to predict an individual’s career collected using LinkedIn, into two class labels named, Position and Domain. Here, Position has 8 labels defining the position names and Domain has 6 labels defining the industry domain. To predict our findings, the career dataset is tested on six different multiclass multiouput classifiers, and among those the best classifier is finalized for our dataset that predicts out defined class labels upto an accuracy score of 90% approximately.
Primary Advisor's Department
Stander Symposium, College of Arts and Sciences
"Career Prediction via Historical Information" (2023). Stander Symposium Projects. 2855.
Presentation: 10:45 a.m.-12:00 p.m., Kennedy Union Ballroom