Python Machine Learning Project - Examining Successful Attribute for Undergraduate - ClickMyProject

Methods for automatically identifying students in need of assistance have been studied for decades. Higher education institutions (HEIs) are often curious whether students will be successful or not during their study.
Before or during their courses the academic institutions try to estimate the percentage of successful students. This study focuses on ways to examining successful attributes for undergraduate students using data mining techniques. The Student Education Data Mining works were organized as a raw data base. As result of the execution of classification processes, a set of educational functionalities was found, a realistic pattern of SEDM approaches was discovered. One key finding is most of the SEDM approaches are ground on a set composed by educational systems, test preparation, tasks, methods, and algorithms each.Student Educational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. The various machine learning and deep learning algorithm models are implemented and examining the attributes for undergraduate student. The prediction result based on accuracy, precision, recall, f1-score, specificity and sensitivity.

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