Abstract
Digitization has immensely impacted our education system and the career planning of students. Generally, career counselors/experts play an important role in evaluating and assisting students in choosing appropriate careers for themselves. These conventional methods and practices are no longer very impactful and after a point, they have proved ineffective. Hence many educational institutions have started using advanced automated solutions that are developed through artificial intelligence. Automation of the counseling system saves effort as well as time and has the potential to reach a diverse group of people. This paper explores the various machine learning algorithms used for providing effective career guidance and counseling. The study works on real-time students’ data using machine learning techniques and considers different attributes to find out which factors play a major role in choosing career students. Decision tree provides the highest accuracy for both datasets.
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Sharma, S., Kumawat, S., Garg, K. (2022). Predicting Student Potential Using Machine Learning Techniques. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_40
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