  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.
