Smart ERP solution for academic eligibility assessment through machine learning
| dc.contributor.author | Nusrat Jahan | |
| dc.contributor.author | Bhoir, Priyanshi [22CO01] | |
| dc.contributor.author | Pathan, Mahenoor [22CO05] | |
| dc.contributor.author | Chavan, Aashish [22CO07] | |
| dc.contributor.author | Solkar, Aarhaan [22CO62] | |
| dc.date.accessioned | 2026-08-20T05:53:42Z | |
| dc.date.available | 2026-08-20T05:53:42Z | |
| dc.date.issued | 2026-06 | |
| dc.description.abstract | The growing volume of data and many educational institutions still relying on manual or rule-based methods pose major challenges in handling and assessing student academic eligibility. These methods can be timeconsuming, error-prone, and lack the ability to predict. This project is based on a Smart ERP Solution for Academic Eligibility Assessment using Machine Learning, which will help automate and improve the process of academic eligibility assessment by incorporating predictive analytics within an ERP system. The main goal of this research is to design a smart system that can process academic data of students and decide student eligibility in a rule-based as well as machine learning based approach. Our system adopts a hybrid approach, combining the institution’s eligibility rules with a Random Forest-based eligibility predictor aimed at enhancing decisionmaking and managing intricate cases. The process includes data gathering, cleaning, feature engineering, model training, hybrid decision-making, and visualizing the results in a user-friendly way for administrators and students. The proposed system was successfully deployed and has shown greater efficiency, lesser manual labour and better accuracy than existing systems. It also facilitates early detection of students in need of intervention. These findings demonstrate the integration of machine learning and ERP system enhances academic eligibility assessment. In summary, this project succeeds in its aim to convert conventional academic management systems into smart, automatic and data-driven systems, thus helping to improve the efficiency and transparency of academic processes. | |
| dc.identifier.uri | http://aiktc.ndl.gov.in/handle/123456789/4564 | |
| dc.language.iso | en_US | |
| dc.publisher | AIKTC | |
| dc.title | Smart ERP solution for academic eligibility assessment through machine learning | |
| dc.type | Project Report |