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Subjective areas of improvement: A personalized recommendation
, , P. Dubey, A.R. Drolia, S. Srihari
Published in Elsevier B.V.
Volume: 172
Pages: 235 - 239
Gen Z-ers, being independent, self-confident and autonomous as their key characteristics are proven to be technologically more advanced than their previous generations. To reach the milestone of understanding and satisfying the Gen-Z community, and to make use of the digital adherence of this generation, a personalized recommender system using machine learning is designed that can recommend the areas which the students should strengthen themselves to lead and be distinct among their peers. The proposed work uses Support Vector Machine to predict the area of improvement which the student need to focus on. The proposed system will also recommend the list of online courses and materials that the users can make use of to strengthen themselves. The model is built using Python flask and Jupyter notebook and is tested using one public dataset and a private dataset. The results are convincing and accurate enough in identifying the proper areas that require improvement. © 2020 The Authors. Published by Elsevier B.V.
About the journal
JournalData powered by TypesetProcedia Computer Science
PublisherData powered by TypesetElsevier B.V.