My work on network traffic anomaly detection involved reviewing machine learning approaches, collecting and preprocessing network traffic data, and analyzing it to support model training and evaluation. This research was published as “Intelligent Network Traffic Anomaly Detection Using ML Algorithms.”
As a Data Analyst Intern and SQL Developer, I gained practical experience developing SQL queries and extracting data from relational database management systems. I also gained experience with reporting workflows that support data analysis and data-driven decision-making.
As an AI Agentic Systems Intern, I gained practical exposure to Generative AI, large language models, and Agentic AI systems. I also researched content-based and collaborative filtering for a Python product recommendation system, preparing product and user interaction data to support model development.
