Portable end-to-end skill pack for Web of Science bibliometric processing, topic modeling, mobility analysis, visualization, and deep-report generation.
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.
A simple Python project using Simple Linear Regression to predict a student's marks based on the number of hours they studied.
Interactive collection of matrix-based algorithms implemented in Java, covering matrix operations, academic and business data analysis, numerical patterns, and magic squares
Java-based Student Performance Intelligence System for analyzing academic performance, identifying risks, ranking students, and generating meaningful recommendations.
This project analyzes student performance in the INSY107 course (2020–2023) using Python to uncover trends by gender, department, and year for academic improvement.
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