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@article{178271,
        author = {Sanjeev Kumar and Anjali Yadav and Suraj Singh and Khushi Garg},
        title = {Career Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3217-3222},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178271},
        abstract = {Picking an appropriate career after the 12th grade is a daunting problem for the student whose career selection process is plagued by inadequate counselling and organized information resources.
This paper offers an intelligent and interactive Career Consultation System that aids students of the 12th grade to figure out career choices that best suit their interests, academic background, and their own individuality.
The system uses machine learning algorithms to process user input and then recommends careers based on personalization.
The back end is coded in Python and Flask, whereas the front end is deployed with ReactJS, HTML, and CSS, which assures a smooth, user-friendly interface.
Thus, the system bridges the existing gap between student" scepticism and professional" guidance by providing recommendations generated through data in an economical and scalable format.
Focused recommendations seek to empower students to make informed decisions about their future profession.},
        keywords = {},
        month = {May},
        }