Smart Career Advisor Using Machine Learning

Abstract: Determining the ideal career path is a significant milestone in a student’s life. Unfortunately, students often face a lack of proper career counselling and awareness about options that align with their individual talents and preferences. This paper introduces a Smart Career Advisor built on machine learning principles to address these gaps. Leveraging models like Random Forest and XGBoost, the system analyses user-specific data such as academic records, interests, and skillsets to provide accurate, customized career suggestions for different education levels, including 10th, 12th, Diploma, Graduation, and Post-Graduation. The objective is to empower students with data-driven insights to make informed decisions about their careers.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-04-27
DOI
https://doi.org/10.22214/ijraset.2025.69537
Citations
1
Primary Topic
Smart Systems and Machine Learning
Type
article
Field-Weighted Citation Impact
1.49
Controls
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article

Smart Career Advisor Using Machine Learning

Mrs. D. Urlamma
1 citations
International Journal for Research in Applied Science and Engineering Technology
Smart Systems and Machine Learning
1.49
article

Smart Career Advisor Using Machine Learning

Mrs. D. Urlamma
article en
1 citations

Abstract

Abstract: Determining the ideal career path is a significant milestone in a student’s life. Unfortunately, students often face a lack of proper career counselling and awareness about options that align with their individual talents and preferences. This paper introduces a Smart Career Advisor built on machine learning principles to address these gaps. Leveraging models like Random Forest and XGBoost, the system analyses user-specific data such as academic records, interests, and skillsets to provide accurate, customized career suggestions for different education levels, including 10th, 12th, Diploma, Graduation, and Post-Graduation. The objective is to empower students with data-driven insights to make informed decisions about their careers.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(4)
Openalex Percentile: Top 18%
Smart Systems and Machine Learning
1.49
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