Computational Models for Intelligence and Automation
Intelligent IT Career Advisor using Machine Learning
Authors: Hina Bediya, Dharmesh Mishra and Nikhita Mangaonkar
Publishing Date: 28-08-2025
ISBN: 978-81-975670-1-8
Abstract
The tremendous growth in the Information Technology (I.T.) field has made choosing a job more difficult than ever before, calling for intelligent, data-driven solutions to close the skills gap and meet business demands. This research study presents an innovative career advisory system that leverages the K-Nearest Neighbors (KNN) algorithm to recommend IT roles tailored to user skill profiles. Using proficiency ratings of skills across 17 IT domains like Cybersecurity, Database Management, Software Development, etc., in a dataset of 9178 entries, the model will provide accurate and personalized career advice. The model provides career predictions for positions like database administrator, software developer, business analyst, etc., by analysing both self-assessed skills and objective knowledge evaluations. This results in a classification accuracy of 98 percent, supported by robust cross-validation (0.83 ± 0.03) and high precision, recall, and F1-scores across all job roles. While addressing user input uncertainties, the dual-assessment system guarantees strong suggestions. The system, which targets IT students, fresh graduates, and professionals who are looking to make a career shift, demonstrates the power of machine learning in aligning individual competencies with the ever-changing demands of the job market. This research points out the importance of including intelligent systems in career planning in order to provide users with useful information and encourage well-informed choices in a constantly changing business environment.
Keywords
Machine Learning, career path prediction, IT careers, skill assessment, personalized guidance, k-nearest neighbors.
Cite as
Hina Bediya, Dharmesh Mishra and Nikhita Mangaonkar, "Intelligent IT Career Advisor using Machine Learning", In: Puneet Kumar Gupta (eds), Computational Models for Intelligence and Automation, SCRS, India, 2025, pp. 48-58. https://doi.org/10.56155/978-81-975670-1-8-5