Every educational institute feels proud when its admission closes with expected number of students. The prospective student enters the campus with lots of hopes, dreams and expectations. When their expectations are not met or if they undergo for critical circumstances and makes them drop from their registered program. Predicting undergraduate student dropouts are a major challenge in educational system due to the multidimensionality of data. This paper focuses on dimensionality reduction of multi-behavioral attributes of a 150 students with 51 attribute to identify the factor that affects the early dropout. The dataset dimensionality is reduced through Principal Component Analysis by obtaining the Eigenvalues and Eigenvectors from the covariance matrix by transforming the original attribute into new set attribute without losing the information. Visualization is done with a help of R package factoextra and FactoMineR. The further dataset can be used for classification. The discovery of concealed knowledge can be used for better academic planning and early prediction of student dropout. © 2016 IEEE.
cited By 0; Conference of 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016 ; Conference Date: 15 December 2016 Through 17 December 2016; Conference Code:127661
V. Hegde, “Dimensionality reduction technique for developing undergraduate student dropout model using principal component analysis through R package”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, 2017.