Shahryar Khan

Developed a recommendation system using clustering techniques and deep learning to identify popular customer products. Built an end-to-end analytical tool with PandasAI and GPT-3.5/4 for business insights, integrating a scalable pipeline for real-time updates.

Led the development of a state-of-the-art recommendation system and deep learning models at CIC IBM. Utilized Python, Flask, and GPT-4 to transform data into actionable insights, enhancing decision-making processes and delivering exceptional project outcomes.

Worked on optimizing the discovery and development of medicine molecules using clustering and deep learning techniques. Designed LLM-based pipelines for data extraction and consolidated datasets for efficient analysis.

Improved an existing recommendation system for marketing campaigns using Naïve Bayes and Random Forest. Enhanced SQL scripts for data retrieval and created predictive scoring mechanisms to boost campaign effectiveness.

Grade: 3.7

Completed a Bachelor of Science in Information Technology, focusing on various aspects of IT including programming, data analysis, and system design. Achieved a GPA of 3.7, demonstrating strong academic performance and a solid understanding of information technology principles.