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@article{159247,
        author = {Kotagiri Sanjana and Bommidi Madhan Sainath Reddy and T Sri Pranith Reddy and Raheem Unnisa},
        title = {STUDENT PERFORMANCE ANALYSIS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {683-691},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159247},
        abstract = {Performance analysis of outcomes based on learning is a system that strives for excellence at different levels and diverse dimensions in the field of students’ interests. This paper proposes a complete EDM framework in the form of a rule-based recommender system that is designed not only to analyze and predict the performance of students, but also to present the reasons behind it. Does the proposed framework analyze the students? To get all the necessary information about students, teachers, and parents, we collect demographic information, study-related characteristics, and psychological characteristics. Using powerful data mining techniques, to predict academic performance with the highest accuracy possible. The framework succeeds to highlight the student’s weak points and provide appropriate recommendations. The realistic case study that has been conducted on 200 students proves the outstanding performance of the proposed framework in comparison with the existing ones. Student Performance Analysis using Machine Learning.},
        keywords = {},
        month = {},
        }