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A Pathbreaking Analyzer for Higher Education.
- Source :
- International Research Journal of Innovations in Engineering & Technology; Oct2023, Vol. 7 Issue 10, p220-229, 10p
- Publication Year :
- 2023
-
Abstract
- HEZER, a ground-breaking analyzer for higher education that aims to improve the decision-making process for people starting their educational journeys. The developed system makes use of web scraping techniques and cutting-edge machine learning algorithms to deliver individualized recommendations, with an emphasis on solving issues like limited advice, financial limits, career possibilities, and limited options. Students are helped in choosing their ideal degree programs by insightful opinions from both newcomers and professionals in the sector that have been gathered through surveys and questionnaires. HEZER includes a full range of functionality, such as budget planning, financial estimation, career analysis, and loan payback predictions. It is powered by cutting-edge technologies like React.js, Python, and machine learning methods like Random Forest Regression. It allows students to arrange their finances effectively by allowing them to predict future course prices. Carefully crafted research components offer individualized recommendations, financial advice, and perceptive analysis of commercial potential connected to chosen projects. The system's accuracy and dependability have been confirmed through meticulous integration and testing. HEZER establishes a new benchmark for customized recommendation systems in higher education, providing students with crucial tools to negotiate the challenging environment and make well- informed decisions. choices, ultimately leading to successful and fulfilling careers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25813048
- Volume :
- 7
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- International Research Journal of Innovations in Engineering & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 173593224
- Full Text :
- https://doi.org/10.47001/IRJIET/2023.710029