Student Academic Performance Prediction using Supervised Learning Techniques

• DOI: 10.3991/ijet.v14i14.10310
• Publication Date: 2019-07-24T12:09:38Z
• Authors: Muhammad Imran; Shahzad Latif; Danish Mehmood; Muhammad Saqlain ...
• Keywords: C4.5 algorithm; Educational Data Mining; Data pre-processing; Ensemble Learning; Supervised Learning

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This being addressed by mining (EDM). EDM develop methods for discovering that derived from environment. These are used understanding student and their learning The institutions often curious how many students will be pass/fail necessary arrangements. In previous studies, it has been observed researchers have intension on selection appropriate algorithm just classification ignores solutions problems which comes during phases such as high dimensionality ,class imbalance error etc. Such types reduced accuracy model.
 
 Several well-known algorithms applied this domain but paper proposed model based supervised decision tree classifier. addition, an ensemble method improve Ensemble approach designed solve classification, predictions problems. study proves importance preprocessing fine-tuning tasks resolve quality issues. experimental dataset work belongs Alentejo region Portugal obtained UCI Machine Learning Repository. Three (J48, NNge MLP) employed purposes. results showed J48 achieved highest 95.78% among others.