Chris Simmerman
Practical machine learning • systems mindset • measurable impact
I build LLM/RAG applications and machine learning systems with a focus on end-user reliability, observability, and continuous improvement. I also apply data science to transportation and traffic problems, and explore research-style work (network analysis, historical text mining) where I turn complex data into clear narratives and actionable insights.
Projects
Three deep dives (more coming soon) Metro Transit Network Criticality & Resilience
GTFS-directed graphs, centrality, and disruption simulations
PythonNetworkXGTFSFoliumSimulation
Explore the analysis →
Daily Traffic Volume Forecasting for MnDOT Sensors
Global model + rolling backtest; station-weighted evaluation
Pythonscikit-learnTime SeriesBacktestingPlotly
View the write-up →
Breast Cancer Prediction
Logistic Regression + PCA on Wisconsin Diagnostic dataset
Pythonscikit-learnPCAClassification
Open case study →