Analyzing Results using Machine Learning Techniques

Abstract

"Analyzing results using machine Learning techniques" is an innovative, web-based platform designed to transform the management and analysis of academic results in engineering institutions through advanced machine learning algorithms. This project integrates modern front-end technologies with a robust Flask back-end and a PostgreSQL database, ensuring a secure, scalable, and user-friendly system. At its core, the platform features dual access portals tailored for both students and staff. Students can easily retrieve their examination results by entering their unique PRN number, while staff members benefit from secure, role-based access via Google OAuth authentication. A key component of the system is its sophisticated PDF processing module, which automates the extraction, analysis, and categorization of exam data. Leveraging machine learning models, the system not only generates detailed CSV reports—covering class distinctions, top performers, and subject-specific performance—but also provides predictive analytics to offer deeper insights into student achievements. The system further enhances user experience through dynamic batch and year selection interfaces and a minimalist, responsive design that ensures intuitive navigation. Additionally, the platform supports robust staff management capabilities, enabling real-time editing and deletion of user credentials, thereby reinforcing its production-ready architecture and deployment readiness.