A University Recommender System based on Students Profiles

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dc.contributor.authorBarka, Wafa
dc.contributor.authorBouarbi, Mounya
dc.contributor.authorReporter: Amraoui, Noureddine
dc.date.accessioned2023-06-26T08:17:52Z
dc.date.available2023-06-26T08:17:52Z
dc.date.issued2023-06-10
dc.description.abstractAlthough universities are known for their pursuit of knowledge and research, they do not fully exploit the potential of the vast amounts of data they generate and collect. One consequence of this is that future students face a daunting university selection process. This work aims to devise a Recommender System (RS) that can automatically propose the best universities for students which can simplify their selection process and raise their chances of admission. To evaluate the effectiveness and feasibility of our proposed RS, we collect relevant data from various sources. This data includes student profiles, academic qualifications, preferences, and other factors. We analyze this data to evaluate the performance of our recommender system and its ability to generate accurate and useful recommendations. Our obtained results show that our University RS is in average effective in generating personalized recommendations based on three powerful algorithms, namely, K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM).en_US
dc.identifier.urihttp://dspace.univ-msila.dz:8080//xmlui/handle/123456789/39793
dc.language.isoenen_US
dc.publisherUniversity of M'silaen_US
dc.subjectUniversity Selection, Recommender Systems (RS), Machine Learning (ML), Data Processing.en_US
dc.titleA University Recommender System based on Students Profilesen_US
dc.typeThesisen_US

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