Hudson Smith

I’m an applied mathematics student interested in data engineering, software engineering, and building full-stack tools around real-world data.

My work focuses on turning messy, operational datasets into usable systems: cleaning and structuring raw data, designing data pipelines, building predictive models, and creating interfaces that make results easier to explore and act on.

I have hands-on experience with Python, pandas, NumPy, scikit-learn, SQL, APIs, and web development, with coursework in probability theory, linear algebra, real analysis, and statistics supporting the technical side of my projects.

Featured Projects

Road condition prediction dashboard

Predicting road maintenance needs from project and location data.

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 Quests

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