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@article{176024,
        author = {Mr. N. Sendhil Kumar and S. Chandra Kiran Reddy and M. Shyam Sundar and B. Bhanu Prakash and K. Tejasri},
        title = {Socio-Educational Early Warning System for Effective Student Retention},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4769-4774},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176024},
        abstract = {The development of data analysis techniques and intelligent systems has had a considerable impact on education, and has seen the emergence of the field of educational data mining (EDM). The Early Warning System (EWS) has been of great use in predicting at-risk students or analyzing learners’ performance. Our project concerns the development of an early warning system that takes into account a number of socio-cultural, structural and educational factors that have a direct impact on a student’s decision to drop out of school. We have worked on an original database dedicated to this issue, which reflects our approach of seeking exhaustiveness and precision in the choice of dropout indicators. The model we built performed very well, particularly with the K-Nearest Neighbors (KNN) algorithm, with an accuracy rate of over 99.5% for the training set and over 99.3% for the test set. The results are visualized using a Django application we developed for this purpose, and we show how this can be useful for educational planning.},
        keywords = {Student, Early Warning System, KNN, Educational Data Mining Django, Intelligence.},
        month = {April},
        }