A machine learning-based educational technology system that predicts student academic outcomes through three specialized models: final exam mark prediction, dropout risk assessment, and pass/fail forecasting. Built with Python, Flask, and scikit-learn to help educational institutions identify at-risk students and implement timely interventions.

Publication network analyser - co-author graphs, citation trends, topic clusters from Google Scholar

Tracks faculty academic activities, automates API score calculation based on UGC guidelines, and provides dashboards and reports for performance evaluation.

AI-powered academic automation platform built with PostgreSQL, FAISS, FastAPI, React, and adaptive Hybrid + Agentic RAG for grounded conversational querying, semantic retrieval, intelligent agents, analytics workflows, and large-scale academic data processing.

A production-ready MySQL project with 9 relational tables, 400+ records, and 20+ analytics queries for tracking student performance, attendance, and learning outcomes in educational institutions.

A full-stack web application that predicts student academic performance using key indicators like SGPA, CGPA, attendance, and engagement metrics, with role-based dashboards for students, teachers, and administrators.

Unlock academic insights with EduPredict, a Python-based machine learning tool achieving up to 90% accuracy. Train, evaluate, and deploy models locally with ease. Built for educators, students, and researchers aiming to forecast academic success.

Python tool for generating realistic student performance data and comprehensive academic analytics using Pandas and NumPy

Academic Insights provides interactive academic statistics and analyses for students and educators, utilizing modern web technologies. It serves as an essential tool for monitoring educational performance and progress.

A centralized platform to manage and trace data for students, faculty members, and alumni

Data analytics pipeline for academic workload and quota analysis, designed with production-grade structure, reproducibility, and data security best practices.