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@article{178871,
        author = {Jyothi P K and Somesh Kamalakant Hulamani and Spoorthi K M and P. Sushmita Singh},
        title = {A Machine Learning-Based Predictive Analytics Approach to Identifying Student Success Patterns for Early Academic Intervention},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4656-4659},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178871},
        abstract = {Academic performance plays a vital role in shaping students’ future prospects, yet predicting it accurately remains a challenge due to the influence of diverse factors beyond grades and attendance. This paper presents a predictive analytics system designed to identify student success patterns using machine learning techniques. The system incorporates academic, behavioural, and lifestyle features such as study time, internet access, alcohol consumption, and absenteeism. Three supervised learning models—Logistic Regression, K-Nearest Neighbours (KNN), and Support Vector Machine (SVM)—were developed and evaluated. Among these, SVM achieved the highest prediction accuracy and was selected for final deployment. The model was integrated into a user-friendly web application using HTML, CSS, and Flask, enabling real-time predictions and visual insights for educators. The system helps institutions proactively identify at-risk students and initiate timely interventions, improving academic support strategies. By leveraging multivariate data and predictive modelling, this work demonstrates a scalable and effective approach to enhancing educational outcomes through early detection and data-driven decision-making.},
        keywords = {Academic Performance Prediction, Machine Learning Models, Support Vector Machine (SVM), Educational Data Mining, Early Intervention System.},
        month = {May},
        }