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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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An Approach to Improve the Performance of Students Prediction System
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LAP LAMBERT Academic Publishing Aug 2018, 2018
ISBN 10: 6139842018
ISBN 13: 9786139842018
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch. Codice articolo 9786139842018
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An Approach to Improve the Performance of Students Prediction System
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LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139842018
ISBN 13: 9786139842018
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Paperback. Condizione: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock. Codice articolo zk6139842018
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An Approach to Improve the Performance of Students Prediction System
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ISBN 13: 9786139842018
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur PrabhjotPrabhjot Kaur received Master of Technology in Information Technology from Guru Nanak Dev Engineering College, Ludhiana. She completed Bachelor of Technology in Information Technology at Baba Banda Singh Bahadur Engineer. Codice articolo 385873833
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An Approach to Improve the Performance of Students Prediction System
Editore:
LAP LAMBERT Academic Publishing Aug 2018, 2018
ISBN 10: 6139842018
ISBN 13: 9786139842018
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Taschenbuch. Condizione: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch. Codice articolo 9786139842018
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An Approach to Improve the Performance of Students Prediction System
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ISBN 10: 6139842018
ISBN 13: 9786139842018
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9786139842018
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An Approach to Improve the Performance of Students Prediction System
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ISBN 13: 9786139842018
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Taschenbuch. Condizione: Neu. An Approach to Improve the Performance of Students Prediction System | Prabhjot Kaur (u. a.) | Taschenbuch | 80 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139842018 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 114567463
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