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@article{174705,
        author = {Mrs.B. Yazhini and Ms. REVATHI S and Mr. SABARANI R},
        title = {STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1069-1070},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174705},
        abstract = {The project titled "Student Performance Prediction using Machine Learning" aims to leverage machine learning techniques to predict the academic performance of students based on historical data and various academic features. In educational institutions, predicting student performance can help in identifying students at risk, enabling early interventions and personalized learning strategies. The project involves collecting data related to student demographics, previous academic performance, attendance, and other relevant factors. This data will be processed and analyzed using the machine learning algorithm, of Random Forest classification. The performance of this model will be evaluated using metrics such as accuracy, precision, recall, and F1 score. The goal is to build an efficient and accurate predictive model that can assist educators in making data-driven decisions to enhance student outcomes and improve teaching strategies. This system will be implemented using Python},
        keywords = {Machine Learning, Student Performance, Predictive Analytics, Educational Outcomes, Personalized Learning, Data-Driven Insights, Intervention Strategies, Academic Success, Classification Algorithms, Neural Networks, Data Preprocessing, Behavioral Patterns.},
        month = {April},
        }