Shatakshi Gupta

Jaypee Institute of Information Technology

Papers

Total Citations

H-Index

About

Shatakshi Gupta is a researcher whose work sits at the intersection of machine learning, cybersecurity, and applied data science. Her most cited paper, "Performance Metrices of Different Machine Learning Algorithms" (2021), has garnered 3 citations and addresses a critical challenge in digital communication: the detection of spam emails. By systematically evaluating and comparing the effectiveness of various machine learning models—such as decision trees, support vector machines, and neural networks—Gupta provides a practical framework for distinguishing spam from legitimate emails. This work is particularly valuable given that email remains a cornerstone of personal and professional communication, and spam poses ongoing security and productivity threats. Gupta's contribution lies in demystifying which algorithms perform best under different conditions, offering clear performance metrics that help practitioners deploy more accurate and efficient spam filters. Her research not only advances the technical understanding of classification algorithms but also has direct implications for improving cybersecurity and user experience in everyday digital interactions. For students and researchers exploring machine learning applications in cybersecurity, Gupta's work serves as a foundational guide to algorithm selection and performance evaluation.