Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data, Machine learning algorithms are employed to classify and cluster the datasets. Research in this field focuses on understanding the behaviour analysis of students, classifying the students to predict the academic outcome, clustering the students based on various factors that can influence the Performance and many more. Many researchers have recommended a recommender system to achieve the goal of identifying and classifying the students based on their performance. The objective of this book is to classify the students based on their Grade Point Average (GPA) and predicting their performance based on their previous academic history and other influential factors.An Improved Random Forest algorithm is proposed in this book to predict the student performance.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data, Machine learning algorithms are employed to classify and cluster the datasets. Research in this field focuses on understanding the behaviour analysis of students, classifying the students to predict the academic outcome, clustering the students based on various factors that can influence the Performance and many more. Many researchers have recommended a recommender system to achieve the goal of identifying and classifying the students based on their performance. The objective of this book is to classify the students based on their Grade Point Average (GPA) and predicting their performance based on their previous academic history and other influential factors.An Improved Random Forest algorithm is proposed in this book to predict the student performance. 168 pp. Englisch. Codice articolo 9786204982564
Quantità: 2 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Codice articolo 26404342688
Quantità: 4 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand. Codice articolo 409892991
Quantità: 4 disponibili
Da: moluna, Greven, Germania
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data. Codice articolo 668857127
Quantità: Più di 20 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND. Codice articolo 18404342698
Quantità: 4 disponibili
Da: preigu, Osnabrück, Germania
Taschenbuch. Condizione: Neu. Student Academic Progression Using Machine Learning Algorithms | An Investigation on Predicting Academic Progression of Students Using Machine Learning Algorithms | Sujith J. | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204982564 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 122703351
Quantità: 5 disponibili
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Taschenbuch. Condizione: Neu. Neuware -Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data, Machine learning algorithms are employed to classify and cluster the datasets. Research in this field focuses on understanding the behaviour analysis of students, classifying the students to predict the academic outcome, clustering the students based on various factors that can influence the Performance and many more. Many researchers have recommended a recommender system to achieve the goal of identifying and classifying the students based on their performance. The objective of this book is to classify the students based on their Grade Point Average (GPA) and predicting their performance based on their previous academic history and other influential factors.An Improved Random Forest algorithm is proposed in this book to predict the student performance.Books on Demand GmbH, Überseering 33, 22297 Hamburg 168 pp. Englisch. Codice articolo 9786204982564
Quantità: 2 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data, Machine learning algorithms are employed to classify and cluster the datasets. Research in this field focuses on understanding the behaviour analysis of students, classifying the students to predict the academic outcome, clustering the students based on various factors that can influence the Performance and many more. Many researchers have recommended a recommender system to achieve the goal of identifying and classifying the students based on their performance. The objective of this book is to classify the students based on their Grade Point Average (GPA) and predicting their performance based on their previous academic history and other influential factors.An Improved Random Forest algorithm is proposed in this book to predict the student performance. Codice articolo 9786204982564
Quantità: 1 disponibili