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@article{207105,
        author = {Dr. Rama Soni and Dr. Sumati Pathak and Dr. Indra Kumar Pandey and Ms. Madhavi Kaushik and Mr. Satanand Chandrakar},
        title = {Comparative Analysis of Tanagra and Rapid Miner in The Context of Higher Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {59-63},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207105},
        abstract = {Data mining technology has significantly improved the ability to extract, store, and interpret enormous quantities of data, even different kinds of data samples. The prediction of students’ academic performance is one of the most fascinating new pathways in the area of educational data mining. [1]. The quality of education plays a critical role in shaping an individual's future and a nation's prosperity. However, student academic performance is influenced by a wide variety of factors, not limited to classroom learning. These include individual behavior, family background, school environment, and access to learning resources. [2]. Higher education plays a crucial role in shaping the future of students and contributing to the development of a nation. Institutions aim to create an environment conducive to quality learning and academic excellence. [3]. In this research paper, we aim to evaluate the accuracy of student performance using data mining algorithms such as Decision Tree (J48), Random Forest, K-NN (IBK), Naïve Bayes, and Neural Networks or Multi-Layer Perceptron (AutoMLP). We will assess student performance and identify which algorithm provides the most accurate results, as well as determine the best-performing model from the dataset. Through this study, both students and institutions can measure their performance and also work towards improving it.},
        keywords = {Tanagra, Rapid Miner, Higher Education, Data Mining, Students Performance.},
        month = {July},
        }