Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{200037,
        author = {Swapna Shirole and Kshitija Raskar and Tejaswini Chaure and Prof.Santosh Pandure},
        title = {Personalized Career Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {87-97},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200037},
        abstract = {Choosing the right career is a critical decision for students, influenced by factors such as academic performance, personal interests, skills, and preferences. Making an informed choice can be challenging due to the wide range of career options available and the lack of personalized guidance. This research addresses this challenge by proposing a machine learning-based career recommendation system that assists students in identifying suitable career paths based on their individual attributes.
The system uses a dataset containing student information, including academic marks, hobbies, skills, and interests. Data preprocessing techniques, such as handling missing values, normalization, and categorical encoding, are applied to ensure the dataset is clean, consistent, and ready for modelling. The Decision Tree algorithm is employed to analyse the relationship between student attributes and potential career options, providing a structured and interpretable prediction model.
The performance of the system is evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that the proposed system can effectively provide personalized career guidance, supporting students and educators in the decision-making process. Overall, this study demonstrates the potential of machine learning to enhance career planning and promote informed professional development.},
        keywords = {Machine Learning, Career Recommendation, Classification, Decision Tree, Random Forest, KNN, Naïve Bayes.},
        month = {May},
        }