Student Future Prediction Using Machine Learning

Authors

  • Dileep Chaudhary   Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India
  • Harsh Prajapati   Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India
  • Rajan Rathod   Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India
  • Parth Patel   Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India
  • Rajiv Kumar Gurjwar   Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT1952300

Keywords:

Career Prediction, Machine Learning, Academic Performance, Linear Regression, Decision Tree Regression

Abstract

Selecting an appropriate career is one of the most important decisions and with the increase in the number of career paths and opportunities, making this decision have become quite difficult for the students. According to the survey conducted by the Council of Scientific and Industrial Research's (CSIR), about 40% of students are confused about their career options. This may lead to wrong career selection and then working in a field which was not meant for them, thus reducing the productivity of human resource. Therefore, it is quite important to take a right decision regarding the career at an appropriate age to prevent the consequences that results due to wrong career selection. This system is a web application that would help students studying in high schools to select a course for their career. The system would recommend the student, a career option based on their personality trait, interest and their capacity to take up the course.

References

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Dileep Chaudhary, Harsh Prajapati, Rajan Rathod, Parth Patel, Rajiv Kumar Gurjwar, " Student Future Prediction Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1104-1108, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952300