Work

Student Segmentation System

Year: 2026

Type: article

Abstract: Developed an AI-powered student segmentation system using Machine Learning and K-Means clustering toanalyze student learning behavior and performance patterns. Performed data preprocessing, feature scaling,clustering, and PCA visualization using Python, Pandas, NumPy, and Scikit-learn. Built an interactive Streamlitdashboard for dataset upload, cluster analysis, visual insights, and downloadable analytics reports.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20324327

Author: Modi, Dev

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W7162004353

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