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@article{206365,
        author = {Dr. Kavita Yogesh Dhakad and Dr. Dipali Meher},
        title = {Automated Syllabus Classification in Computer Science: A Machine Learning Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {1483-1491},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206365},
        abstract = {This study aims to develop a comprehensive and structured repository of undergraduate Computer Science syllabi from universities, autonomous institutions, and affiliated colleges across Maharashtra. The primary objective is to systematically collect and organize these syllabi, primarily sourced from institutional websites using web crawling techniques. Leveraging advanced text mining methods, the collected syllabi are transformed into a curated dataset designed for training machine learning models. This dataset is then utilized for automated classification of Computer Science courses through state-of-the-art text classification algorithm. The outcomes of this research are expected to enhance transparency, accessibility, and consistency in Computer Science curricula across the region. Additionally, the resulting dataset will serve as a valuable resource for educators, researchers, and policymakers, promoting the integration of machine learning in educational data analysis and supporting data-driven curriculum development and evaluation. Machine learning models are designed and implemented through Python. The Best fit model is evaluated.},
        keywords = {text mining, text classification, web crawling, machine learning, support vector machine, support vector machine classifier},
        month = {July},
        }