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@article{173516,
        author = {Shreya Udaysing Bachche and Sheetal Dipak Jadhav and Apeksha Mahesh Bhosale and Vaishnavi Uttam Chougule and Samiksha Anil Patil and Gayatri Sagar Sutar},
        title = {Student Career Prediction System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {543-545},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173516},
        abstract = {Students often face multiple challenges such as peer pressure and expectations from parents during their educational journey. As a result, many students drop out or struggle to progress to the next level of education for various reasons. A proposed approach leverages machine learning techniques like Decision Trees, Random Forests, Support Vector Machines, and AdaBoost to predict a student’s potential career path. Implemented using Python, the system aids students in selecting a suitable course based on their personality traits, interests, and academic abilities. Research indicates that students frequently feel confused about their career options, which can lead to misguided career choices and reduced workforce productivity. The goal of the system is to provide students with the tools to make well-informed decisions about their future careers.},
        keywords = {Random Forest, Career Prediction, Machine Learning},
        month = {March},
        }