A statistical analysis and predictive modeling project in R, examining how lifestyle and clinical factors relate to diabetes risk using real public health survey data. Combines formal hypothesis ...

We employed a multi-step feature selection strategy combining L1-regularized logistic regression with Recursive Feature Elimination (RFE) to identify key stable features associated with adverse ...

A Machine Learning and Natural Language Processing (NLP) project designed to identify signs of depression and mental health risk from social media posts. The system analyzes textual content, extracts ...

Developed in Python with LangGraph and Streamlit, the system translates user questions into optimized SQL queries, validates them with dry-run checks, enforces guardrails such as partition filters, ...

The architecture offers a tiered approach with three distinct access levels: "Basic" mode is designed for users with SQL skills who prioritize rapid data discovery through Dremio. "Expert" mode ...

Our three-level multinomial logistic framework, trained separately for each possible outcome and combined through a principled normalization procedure, was evaluated on held-out data. The framework ...