Abstract :

Selecting An Appropriate Career Is A Challenging Task For Students Because Decisions Are Often Influenced By Limited Awareness, External Opinions, Or Inadequate Professional Guidance. Traditional Career Counseling Approaches Generally Fail To Consider An Individuals Complete Skill Profile And Rarely Provide Personalized Recommendations. This Work Presents Skill2Career, An AI-driven Career Recommendation System That Analyzes User Competencies Across Multiple Professional Domains And Suggests Suitable Career Paths Using A Hybrid Recommendation Approach. The Proposed Framework Integrates A Supervised Machine Learning Classifier With Similarity-based Matching To Improve The Accuracy And Reliability Of Recommendations. In Addition To Identifying Suitable Careers, The System Evaluates Skill Gaps, Estimates Career Readiness, And Provides Personalized Learning Guidance To Help Users Strengthen Missing Competencies. A Web-based Platform Has Been Developed Using Python And Flask To Offer An Interactive Interface For Skill Assessment, Recommendation History, Career Comparison, And Report Generation. Experimental Evaluation Demonstrates That Combining Machine Learning With Similarity Analysis Produces More Meaningful And Personalized Recommendations Than Conventional Single-method Approaches. The Proposed System Serves As An Effective Career Guidance Tool That Supports Informed Decisionmaking And Encourages Continuous Skill Development For Students And Early-career Professionals. Keywords— Career Recommendation System, Artificial Intelligence, Machine Learning, Logistic Regression, Cosine Similarity, Skill Gap Analysis, Flask, Career Guidance.

Published:

07-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1912 - 1918

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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