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@article{209581,
        author = {Mukesh DM and Dr.Paavai Anand G},
        title = {AI BASED PREDICTIVE ANALYTICS FOR STUDENT PERFORMANCE IMPROVEMENT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2256-2262},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209581},
        abstract = {Student performance analysis is an important part of academic monitoring, as it helps identify the progress and learning needs of students. Traditional methods mainly depend on manually reviewing academic records such as attendance, examination marks, assignment scores, and internal assessment marks. This paper presents an AI-Based Predictive Analytics for Student Performance System that uses machine learning to analyze academic information and predict student performance. A synthetic dataset containing student academic attributes is used for the development and testing of the system. The data is cleaned and preprocessed, and relevant features are selected before applying the Random Forest algorithm. The trained model is used to predict the academic performance of students based on the available input data. The results are analyzed using performance measures and graphical representations to understand the prediction capability of the model. The proposed system can help identify students who may require additional academic attention and support academic performance monitoring. Since synthetic data is used, the system serves as a demonstration of the proposed prediction approach and can be further improved using larger real-world datasets.},
        keywords = {Support Vector Machine, Integrated Development Environment, Mean Squared Error},
        month = {October},
        }