Students Performance prediction by EDA analysis and Hybrid Deep Learning Algorithms

Abstract

Education is a pillar of any individual to attain success in their life. Knowledge evaluate students’ performance which resulted with low accuracy and many algorithms not able to manage imbalanced dataset. This research utilized the ML algorithms, EDA development and learning makes everyone become educated person. Many universities and colleges lend graduate course of study for various disciplines, and students choose courses based on interest. At the same time many researches consider normal factors like, personal and academic features, experimented with many machine learning models and analysis and Hybrid algorithms for students’ performance prediction. Exploratory data analysis performed to identify the correlation between features and features which support the evaluation of student’s performance prediction. Based on the evidence from the EDA analysis this paper aims to provide a deep learning-based hybrid approach that consists of Deep Neural Network -Random Forest (DNN-RF), Deep Neural Network -Light GBM (DNN-Light GBM) algorithms to evaluate the students' performance prediction that capable of handling a wide range of datasets from small to enormous and improve the prediction accuracy. The results shows that the Deep Neural Network -Random Forest achieved an accuracy of 99.56%, precision of 97.82%, recall of 98.13%, f1 score of 98.95% and DNN-Light GBM attained an accuracy of 90.76%, 85.13%, 84.94%, 87.93%. while comparing to ML algorithms RF, Light GBM and DNN-Light GBM, DNN-RF is utmost effective algorithm for forecasting student performance.