An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.

Built a pipeline using stats + SHAP to detect grading bias and evaluate teacher impact via attendance and marks data. Identified sensitive attribute influence (e.g., gender/religion) on student performance using explainable AI.

ResultOps is a robust, university-grade result processing platform built using Streamlit and Firebase Firestore. The application is designed to streamline transcript parsing, validation, storage, and analysis for academic institutions.

A complete Power BI Student Result Analysis Dashboard with toppers, KPIs, subject insights, mark ranges, and data modeling using Excel, Power Query & DAX.

🎓 Student Performance Prediction System using Machine Learning & Streamlit to forecast next semester CGPA with interactive insights and real-time predictions.

a web app that uses OCR and LLMs to extract and analyze student grades from PDF transcripts. It helps automate parsing, provides insightful analytics, and visualizes academic performance across semesters and subjects.

Time-series forecasting benchmark: SARIMA, ARIMAX, Prophet, CNN on student interaction data

Simulates student activities using a simplified Probabilistic Finite Automaton (PFA) model for recommender system studies.

End-to-end machine learning system that predicts student performance using academic and engagement data, with FastAPI backend and Next.js dashboard.

📊An end-to-end Power BI analytics dashboard providing deep insights into Indian Higher Education. Visualizes college distribution by region, management type, student enrollment strength, and course offerings across multiple states and cities.

Analyzes student behavior patterns to understand their impact on academic performance. Provides clear visual insights and correlations from real data. Supports early prediction and decision-making for improving student outcomes.

machine learning web app that predicts students’ math exam scores using demographic and academic factors. Built with Flask, HTML/CSS, and a Random Forest model trained on the Student Performance dataset. Interactive, insightful, and easy to use.

📊 Analyze student performance with this interactive Power BI dashboard featuring KPIs, insights, and detailed documentation for easy replication.

Multi-year benchmark analysis comparing EHCP phonics attainment in Medway against national averages, highlighting persistent performance gaps and system-level risk factors.

A machine learning project to predict student final grades using academic and demographic data. Built with pandas, scikit-learn, and visualized with seaborn and matplotlib to gain insights and support early intervention for students.

A Power BI dashboard analyzing online course sales, enrollment trends, student demographics, revenue performance, completion rates, and instructor effectiveness for an e-learning platform.

Predict student performance using ML and Streamlit

A single‑page, animated dashboard built with Chart.js to showcase your 100‑student survey.

Machine Learning app predicting Student GPA based on study habits, attendance, and parental support. Built with Streamlit and Scikit-Learn to help educators identify at-risk students.

platformă pentru studenții ase cu predicții pentru performanțe academice și analiza progresului - hostată pe un raspberry pi 5