An Approach to Improve the Performance of Students Prediction System - Softcover

Kaur, Prabhjot; Kamra, Amit

 
9786139842018: An Approach to Improve the Performance of Students Prediction System
Softcover
ISBN 10: 6139842018 ISBN 13: 9786139842018
Verlag: LAP LAMBERT Academic Publishing, 2018

Inhaltsangabe

Data generated by educational settings can be used to predict the future of students. The data represented by various features was taken from University of California Irvine repository. Preprocessing and transformation of data was performed before training. For transformation, nominal data is converted to numerical form. Data mining algorithm from decision tree, neural network, support vector machine and regression were selected. Algorithms used were Simple Logistic Regression, Linear Regression, Sequential Minimal Optimization, Random Forest and Multilayer Perceptron. Algorithms were evaluated with 10-fold cross validation and standardized before training. Best algorithms were selected with highest accuracy and lowest root mean square error. The different methods are proposed to improve the performance of selected best algorithms. Accuracy of the best classifier was improved by using feature selection. Root mean square error of best algorithm was reduced by resampling. Using ensemble method, accuracy was increased and root mean square error was reduced to lowest possible value. Present and existing work is compared and it shows that higher accuracy and lowest error is achieved.

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Reseña del editor

Data generated by educational settings can be used to predict the future of students. The data represented by various features was taken from University of California Irvine repository. Preprocessing and transformation of data was performed before training. For transformation, nominal data is converted to numerical form. Data mining algorithm from decision tree, neural network, support vector machine and regression were selected. Algorithms used were Simple Logistic Regression, Linear Regression, Sequential Minimal Optimization, Random Forest and Multilayer Perceptron. Algorithms were evaluated with 10-fold cross validation and standardized before training. Best algorithms were selected with highest accuracy and lowest root mean square error. The different methods are proposed to improve the performance of selected best algorithms. Accuracy of the best classifier was improved by using feature selection. Root mean square error of best algorithm was reduced by resampling. Using ensemble method, accuracy was increased and root mean square error was reduced to lowest possible value. Present and existing work is compared and it shows that higher accuracy and lowest error is achieved.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2018
Sprache
Englisch
ISBN 10 
6139842018
ISBN 13 
9786139842018
Einband
Tapa blanda
Anzahl der Seiten
80
Herausgeber
Singh Jagdeep
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar