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@article{209074,
        author = {Ghanashyam K Suresh Kumar and Abhinand Babu PK and Goutham Krishna M and Vidwath},
        title = {Students Performance Prediction System Using Machine Learning with Advisory Chatbots and PDF Marks Validation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {568-572},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209074},
        abstract = {Educational institutions need to know which students are performing well and which students need support before final examinations. In many colleges, marks are still maintained in PDF sheets and reviewed manually, so weak performance is noticed late and data-entry errors such as marks exceeding the maximum are easily missed. This work presents a Students Performance Prediction System in which a trained model places each learner in a performance band labelled Good, Average or Poor, instead of estimating an exact mark. For each predicted category, the system generates advice that tells the student what to do next. The application provides separate logins for teachers and students: only teachers can run performance predictions, while students may only look up their personal marks, band and recommendations. Separate chatbots assist teachers and students with their respective tasks. In addition, a PDF marks validator scans teacher-supplied mark sheets for impossible values, such as 100 marks recorded out of 25, lets the teacher fix them by hand, and generates a new corrected PDF. Together, these components provide a single platform for reliable marks management, performance prediction and student guidance.},
        keywords = {Academic Advisory System, Chatbot, Data Validation, Educational Data Mining, Machine Learning, Role-Based Access Control, Student Performance Prediction.},
        month = {October},
        }