Smart ERP solution for academic eligibility assessment through machine learning
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Date
2026-06
Journal Title
Journal ISSN
Volume Title
Publisher
AIKTC
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.