[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117750-en":3,"doc-seo-117750-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117750,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique - Research Paper","Digital transformation in education enables analysis of teaching and learning parameters to predict global university ranking outcomes. The research investigates the Quacquarelli Symonds (QS) framework and develops machine learning regression models to forecast global rankings. Exploratory data analysis is used to understand dataset behavior, variables, and statistical characteristics prior to evaluating regression algorithms. The study also outlines future work by extending predictive modeling to classification and clustering techniques for broader outcome evaluation.","Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique  \nDr. Prakash Kumar Udupi 1 1Middle East College,  \nMuscat, Sultanate of Oman [prakash@mec.edu.om](prakash@mec.edu.om)[ ](prakash@mec.edu.om)[www.mec.edu.om](www.mec.edu.om)  \nDr. Vishal Dattana 2  \n2 Middle East College,  \nMuscat, Sultanate of Oman [vishal@mec.edu.om](vishal@mec.edu.om)[ ](vishal@mec.edu.om)[www. mec.edu.om](www. mec.edu.om)  \nDr. Netravathi P.S.3  \n3 Srinivasa University  \nManagalore, Indianethrakumar.ccis@ [srinivasuniversity.edu.in](srinivasuniversity.edu.in)[ ](srinivasuniversity.edu.in)[www.srinivasuniversity.edu.in](www.srinivasuniversity.edu.in)  \nJitendra Pandey 4  \n4 Middle East College,  \nMuscat, Sultanate of Oman [jitendra@mec.edu.om](jitendra@mec.edu.om)[ ](jitendra@mec.edu.om)[www. mec.edu.om](www. mec.edu.om)  \nAbstract  \nDigital transformation in the field of education plays a significant role especially when used for analysis of various teaching and learning parameters to predict global ranking index of the universities across the world. Machine learning is a subset of computer science facilitates machine to learn the data using various algorithms and predict the results. This research explores the Quacquarelli Symonds approach for evaluating global university rankings and develop machine learning models for predicting global rankings. The research uses exploratory data analysis for analysing the dataset and then evaluate machine learning algorithms using regression techniques for predicting the global rankings. The research also addresses the future scope towards evaluating machine learning algorithms for predicting outcomes using classification and clustering techniques.  \nKeywords: Digital Transformation, Teaching and Learning, Machine Learning, Regression, Classification, Clustering.  \n1. Introduction  \nDigital transformation facilitates organisations to understand their data and information, analyse these data for improve operational performances and competitive advantages using digital technologies (Peter et al., 2021) . Data science as a part of digital transformation enables the organisations to generate new business models, develop strategy, create roadmap and build competitive advantage based on the understanding of information and patterns contained within the data. In order to establish new benchmark in the higher education domain, it is also important that the higher educational institutes needs to understand the best practices from the global context, indicators used for measuring performances and evaluation criteria (Vitenko et al., 2021) .  \nMachine learning is a subset of artificial intelligence, which facilitates machine to learn from data using algorithms and predict the results without explicitly  \nprogramming (Awad & Khanna, 2015) . Machine learning helps the organisation to study the business operations, customer behaviours, analyse the patterns derived from the data, develop predictions and prepare relevant strategies. Machine learning problems are broadly classified as classification, clustering and regression problems (Sarkar,2021) . Classification and regression are categorised under supervised learning and clustering is  \ncategorised under unsupervised learning (Alloghani et al., 2020) . If the output variable is discrete, then classification or clustering can be applied, whereas if the output variable is continuous, then regression technique can be applied. Hence, machine learning technique can be used to study and learn historic global university ranking data, identify the patterns and predict the university rankings (Estrada & Cantu, 2022) . Further, global ranking of top universities are continuous and regression technique is most suitable.  \n2. Study of machine learning regression framework  \nMachine learning framework begins with data gathering, and data pre-processing as shown in the below figure 1.  \nFigure 1. Machine learning regression technique framework ","cbCaig98g5weqyh5","https://ap.wps.com/l/cbCaig98g5weqyh5","pdf",1530297,1,4,"English","en",105,"# Abstract\n# Introduction\n# Study of machine learning regression framework\n# Study of existing world university ranking framework\n# Data collection, pre-processing and exploratory analysis\n# Machine learning regression model development","[{\"question\":\"What ranking system does the research use as the evaluation basis?\",\"answer\":\"The study uses the Quacquarelli Symonds (QS) approach for evaluating global university rankings and for building prediction models.\"},{\"question\":\"Why is regression chosen for predicting global university rankings?\",\"answer\":\"Global ranking values are treated as continuous outputs, so regression techniques are considered the most suitable for predicting outcomes.\"},{\"question\":\"What role does exploratory data analysis play in the study?\",\"answer\":\"Exploratory data analysis is performed after feature selection to examine dataset behavior, variable characteristics, and statistical parameters before applying machine learning algorithms.\"}]","Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique - 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