Academic Progression Students Using by Sujith (13 results)

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Condition: New.

Taschenbuch. Condition: 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 148 pp. Englisch.

Taschenbuch. Condition: 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. 148 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Condition: 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.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. Academic Progression of Students 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 9786204983929 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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 Print on Demand.

Taschenbuch. Condition: 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.