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@article{191698,
        author = {Nukala Bhavya Sri and Butta Maheswari and H C Meghana and B RAJESHWARI and Dr.C.V.Madhusudan Reddy},
        title = {Predictive Placement Readiness and Career Insights Framework Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7795-7800},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191698},
        abstract = {Campus placement is essential for judging the academic competency of educational institutions. Conventional methods of campus placement assessment consider by taking into account very few academic parameters and do not provide a live analysis of the readiness of students. This paper introduces a new AI-Powered Placement Prediction & Readiness Assessment System that utilizes a multi-parameter assessment technique to judge the placement feasibility of students. The system incorporates ML algorithms in combination with ATS-level resume parsing technologies, answer forms, and academic parameters that provide a prediction algorithm for campus placement readiness, in addition to pinpointing at-risk students at a very early stage. A Random Forest Classifier algorithm calculates placement scores, while a hybrid rule-based-NLP resume parser extracts attributes from certified resumes. The system offers live analysis in the form of a results platform for students and Training & Placement Officers.},
        keywords = {Placement Prediction, Resume Parsing, Machine Learning, ATS Score, Student Analytics, Risk Classification.},
        month = {January},
        }