GRADE PREDICTING SYSTEM
| dc.contributor.author | N. Zhailau | |
| dc.date.accessioned | 2024-01-23T06:07:50Z | |
| dc.date.available | 2024-01-23T06:07:50Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Abstract Nowadays, prediction of academic performance became necessary for educational entities and universities. As you know most higher educational institutions have a portal system that monitors academic performance. This is necessary in order to assist at-risk students and ensure their retention, as well as to provide exceptional learning resources and experiences, as well as to improve the university’s rating and reputation. Predictive analytics employed advanced analytics, including machine learning implementation, to improve achievement and to generate high-quality performance. As a result, the primary goal of this project is to demonstrate the feasibility of training and modeling on a small dataset size, as well as the feasibility of developing a prediction model with a credible accuracy rate. Using visualization and clustering algorithms, this study investigates the possibility of identifying the key indicators in the small dataset that will be used to create the prediction model. | |
| dc.identifier.citation | N. Zhailau / GRADE PREDICTING SYSTEM / 2022 International Young Scholars' Conference | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1142 | |
| dc.language.iso | en | |
| dc.publisher | 2022 International Young Scholars' Conference | |
| dc.subject | Grade Prediction | |
| dc.subject | Machine Learning | |
| dc.subject | Predictive system | |
| dc.subject | Algorithm techniques | |
| dc.subject | 2022 International Young Scholars' Conference | |
| dc.subject | №11 | |
| dc.title | GRADE PREDICTING SYSTEM | |
| dc.type | Other |