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.

A Machine Learning project that predicts student math performance using features like reading score, writing score, parental education, lunch type, and test preparation course. Built with Python, Scikit-learn, Pandas, and Streamlit, featuring data preprocessing, model training, evaluation, and an interactive web app.

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

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.

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.

Higher education marketing analytics case study using KPI analysis, program performance evaluation, conversion metrics, and executive dashboards to drive business decisions.

🎓 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.

Python-based exploratory data analysis project examining student performance data using Jupyter Notebook, Pandas, NumPy, statistical analysis, visualisations, correlation analysis, and outlier detection to uncover trends and insights across academic subjects.

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

Student Exam Performance Analysis using Power BI and Power Query

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.

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

Predicting student academic success using machine learning. Includes data preprocessing, model comparison (Random Forest, KNN), and feature importance analysis with 89% accuracy.

Assessment analytics project using synthetic gradebook data, R reporting, SQL extracts, and static dashboards.

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

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.

Power BI business intelligence app analysing Udemy course completion rates, NPS performance, and learner engagement using DAX, KPI cards, scatter plots, and interactive dashboards to identify courses requiring improvement.

Public portfolio for analytics systems, synthetic education data, dashboards, and AI-assisted reporting.

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