Hudson Smith

I’m an applied mathematics student focused on applied data analysis, forecasting, and interpretable models using real-world data.

My work centers on turning messy operational data into clear, actionable insights. I’m especially interested in demand forecasting, statistical modeling, and methods that balance accuracy with interpretability.

Coursework includes probability theory, linear algebra, and real analysis, alongside hands-on work in Python using pandas, NumPy, scikit-learn, and PyTorch.

Featured Projects

Live data pipeline (preview soon)

Steam CCU ingestion running every 30 minutes.

Ticket sales per year showing COVID impact — COVID-19 introduced a structural break in ticket demand, motivating year-level controls.

Ticket sales by month — Strong seasonal patterns justified month-based feature encoding.

Ticket sales vs temperature — Ticket sales increase with temperature, with diminishing returns.

RMSE comparison across models — Linear regression outperformed a neural network baseline on test RMSE.

Coursework & Tools

Mathematics

Probability Linear Algebra Real Analysis

Used for bias/variance analysis, model assumptions, and uncertainty reasoning.

ML & Data Libraries

pandas NumPy scikit-learn PyTorch

Feature engineering, model training, and evaluation pipelines.

Tools

GitHub VS Code Jupyter

Methods

Regression Time Series Cross-Validation Feature Engineering

Modeling

Linear Models Error Analysis

Workflow

EDA Data Cleaning

How this shows up in my work:

I usually start with simple, interpretable models to build intuition, then layer on complexity only when it actually helps. I spend a lot of time on feature engineering and sanity-checking results so I understand what’s driving the predictions.

Side Quest

Built between climbing sessions (and a lot of debugging).