Academic Progression of Students Using Machine Learning Algorithms : An Investigation on Predicting Academic Progression of Students Using Machine Learning Algorithms

Sujith J

ISBN 10: 620498392X / ISBN 13: 9786204983929

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

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.

Seller Inventory # 9786204983929

Bibliographic Details

• Title: Academic Progression of Students Using Machine Learning Algorithms : An Investigation on Predicting Academic Progression of Students Using Machine Learning Algorithms
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 620498392X
• ISBN 13: 9786204983929
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 238 grams

About this title

Synopsis

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.