master User

I'm building machine learning and AI projects that turn data into practical predictions and recommendations.

In a smoking and drinking prediction project, I benchmarked Logistic Regression, Random Forest, Gradient Boosting, and an ANN on a roughly 991,000-row health and biosignal dataset. I handled label encoding, scaling, train/test splits, correlation analysis, and cross-model evaluation.

I also built a RAG-based movie recommendation system with LangChain, a vector database, and an LLM, implementing embedding creation, semantic retrieval, vector lookup, and prompt construction end to end.

My work at Amazon and Genpact strengthened my data auditing, SLA compliance, issue diagnosis, performance tracking, and client communication. I'm seeking an entry-level AI/ML opportunity where I can deepen my technical skills while applying this analytical rigor to real-world problems.