Work place: Department of CSE, Graphic Era Hill University, Dehradun, India
E-mail: umangarg@gmail.com
Website: https://orcid.org/0000-0002-1815-5794
Research Interests: Computer Architecture and Organization, Intrusion Detection System, Database Management System, Data Structures and Algorithms
Biography
Mr. Umang Garg received the master degree from AKTU, Lucknow, India and perusing Ph.d from Graphic Era Hill University, Dehradun. He is working as an Assistant Professor in Graphic Era Hill University, Dehradun since 2017. His area of Interest includes IoT Security, Intrusion Detection System for IoT, and Machine Learning. He is reviewer of various Journals and Conference Proceeding. He has published many research papers in the reputed journal and the National/ International conferences including Scopus, IEEE and many Patent in his name.
By Neeta Sharma Shanmuganathan Appukutti Umang Garg Jayati Mukherjee Sneha Mishra
DOI: https://doi.org/10.5815/ijmecs.2023.01.04, Pub. Date: 8 Feb. 2023
The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.
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