Student Analyzer and Recommender
| dc.contributor.author | S Heena Kouser 1NH20CE044; Namitha G K 1NH21CE400 | |
| dc.date.accessioned | 2025-05-31T10:07:17Z | |
| dc.date.available | 2025-05-31T10:07:17Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Student analysis and recommendation systems are becoming crucial in aiding individuals to make well-informed decisions regarding their educational and professional trajectories. The integration of machine learning techniques presents a promising advancement in enhancing the effectiveness of these systems. In our data-driven era, machine learning algorithms offer significant potential to transform how student analysis and recommendation systems operate. This project aims to explore the methodologies involved in utilizing machine learning to improve student analysis and recommendations. It will specifically address the selection of appropriate machine learning algorithms, the procedures for collecting and preprocessing data relevant to student guidance, and the identification of metrics to evaluate the performance of the recommendation models developed through machine learning. By focusing on these essential aspects, the study aims to contribute to the broader discourse on how technology can be leveraged to provide more personalized and precise guidance to students navigating the complexities of modern education and career planning. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/19191 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Student Analyzer and Recommender | |
| dc.type | Learning Object |