Feature Snatching and Performance Assessment for Connoting the Admittance Likelihood of student using Principal Component Reduction
M. Shyamala Devi1, Rincy Merlin Mathew2, R. Suguna3
1M. Shyamala Devi, Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu), India.
2Rincy Merlin Mathew, Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia.
3R. Suguna, Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu), India.
Manuscript received on 06 March 2019 | Revised Manuscript received on 14 March 2019 | Manuscript published on 30 July 2019 | PP: 4800-4807 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2286078219/19©BEIESP | DOI: 10.35940/ijrte.B2286.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, engineers are concentrating on designing an effective prediction model for finding the rate of student admission in order to raise the educational growth of the nation. The method to predict the student admission towards the higher education is a challenging task for any educational organization. There is a high visibility of crisis towards admission in the higher education. The admission rate of the student is the major risk to the educational society in the world. The student admission greatly affects the economic, social, academic, profit and cultural growth of the nation. The student admission rate also depends on the admission procedures and policies of the educational institutions. The chance of student admission also depends on the feedback given by all the stake holders of the educational sectors. The forecasting of the student admission is a major task for any educational institution to protect the profit and wealth of the organization. This paper attempts to analyze the performance of the student admission prediction by using machine learning dimensionality reduction algorithms. The Admission Predict dataset from Kaggle machine learning Repository is used for prediction analysis and the features are reduced by feature reduction methods. The prediction of the chance of Admit is achieved in four ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly contributing to the prediction of chance of admit. Thirdly, the Admission Predict dataset is subjected to dimensionality reduction methods like principal component analysis (PCA), Sparse PCA, Incremental PCA, Kernel PCA and Mini Batch Sparse PCA. Fourth, the optimized dimensionality reduced dataset is then executed to analyze and compare the mean squared error, Mean Absolute Error and R2 Score of each method. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the CGPA, GRE Score and TOEFL Score are highly correlated features in predicting the chance of admit. The execution of performance analysis shows that Incremental PCA have achieved the effective prediction of chance of admit with minimum MSE of 0.09, MAE of 0.24 and reasonable R2 Score of 0.26.
Index Terms: Machine Learning, Dimensionality Reduction, MSE, MAE, R2 Score, PCA, Sparse PCA, Incremental PCA, Kernel PCA and Mini Batch Sparse PCA.
Scope of the Article: Machine Learning.