Abstract - Gathering relevant information to predict student academic progress is a tedious task. Due to the large amount of irrelevant data present in databases which provides inaccurate results. Currently, it is not possible to accurately measure and analyze student data because there are too many irrelevant attributes and features in the data. With the help of Educational Data Mining (EDM), the quality of information can be improved. This research demonstrates how EDM helps to measure the accuracy of data using relevant attributes and machine learning algorithms performed. With EDM, irrelevant features are removed without changing the original data. The data set used in this study was taken from Kaggle.com. The results compared on the basis of recall, precision and f-measure to check the accuracy of the student data. The importance of this research is to help improve the quality of educational research by providing more accurate results for researchers.

翻译：总结 - 收集有关信息以预测学生学业进展是一项无聊的任务。由于数据库中有大量不相关的数据提供不准确的结果,目前无法准确测量和分析学生数据,因为数据中有许多不相关的属性和特征。在教育数据挖掘(EDM)的帮助下,信息的质量可以提高。这项研究表明EDM如何利用相关属性和机器学习算法帮助衡量数据的准确性。EDM在不改变原始数据的情况下消除了不相干的特点。本研究中使用的数据集取自Kaggle.com。根据回溯、精确和F-措施比较结果,以检查学生数据的准确性。这项研究的重要性在于通过向研究人员提供更准确的结果来帮助提高教育研究的质量。