# ETL, analysis, and models run against real datasets. The output is dashboards and decision support that answer an actual question.
Collected coursework from Data Science Fundamentals: smaller studies on penguins, PCA, and gradient descent, plus a capstone on academic performance.
Decision-support dashboard for a poultry farm client that analyzes litter survey data to highlight what drives performance and flag farms at risk of underperformance.
Camera-based counter for newly hatched chicks on a conveyor belt, built to handle the overlapping birds that throw off a laser counter. Patent consideration.
Student analytics dashboard built on a Neo4j knowledge graph, looking for patterns in academic habits and engagement.
Capstone system that develops and evaluates persona-consistent LLMs grounded in Hamlet and Macbeth via LoRA fine-tuning, RAG, and a FastAPI + React demo for single-character and multi-model dialogues.
# Start with data quality and reproducible notebooks before the fancy model. Dashboards and Quarto reports stay decision-support tools. They surface signal for a person; they do not act on it.
Python
Pandas
NumPy
Scikit-Learn
TensorFlow
Jupyter
Plotly