welcome to my
portfolio

A two-part networking project. The first is an enterprise network designed and simulated in Cisco Packet Tracer, built from routers, core and distribution switches, and edge devices. The second is a Python automation script that connects to live Cisco devices with Netmiko, captures their running configurations into date-stamped backups every night, and flags configuration drift by comparing each day against the last. It handles timeouts and authentication failures gracefully and reports a summary of every run. Built alongside earning my CCNA certification. It showed me how much automation improves reliability in network operations, and how catching configuration drift early can prevent an outage.

• Cisco
• Python
• Netmiko

A machine learning study, built with two teammates, testing whether a neighborhood's wealth and demographics can predict local crime. From six public Tucson GIS datasets, we built a Python pipeline to clean, merge, and normalize neighborhood income, population, age, race, and education data against 2024 police incident records, then trained two elastic net regression models: one for the likelihood of violent crime, one for the likelihood a weapon was involved. Each was tuned with 10-fold cross validation and evaluated using R-squared, MSE, RMSE, and residual analysis. I found and assembled the datasets, trained and evaluated the model for our first hypothesis, wrote the introduction, and helped shape the questions we set out to answer. Our original theses did not have enough usable data behind them, so we rebuilt the entire project around new questions and a new dataset partway through. That reset taught me the most: how to scope a data problem around the data that actually exists, and how to read a weak result honestly instead of forcing a story the numbers do not support.

• Python
• scikit-learn
• Machine Learning

A data project with the Arizona Baseball Software Development Team, where I was project manager leading a group of eight. We built predictive performance models from TrackMan batted-ball data, monitoring the TrackMan system and collecting stadium measurements from Google Earth, which I processed. I wrote the Python analysis and engineered an "xHR" metric that estimates home run likelihood across more than 60 NCAA stadiums, and the scalable formula behind it was adopted program-wide to support data-driven player development. I also produced hitting reports for the coaching staff and led many of the team's working calls. It taught me how to turn noisy real-world data into a metric an entire program relies on, and how to lead a technical team toward a shared goal.

• Python
• Analytics
• TrackMan

A command-line application for running a restaurant from the perspective of a manager, host, and server. It handles the full floor workflow: seating customers, assigning servers to tables, taking and modifying orders against a live menu, splitting checks, collecting tips, and applying happy hour pricing, with manager tools for hiring, sales tracking, and secure login. Built in Java with an MVC structure and object-oriented design, and written entirely by hand without GenAI. It was a four-person group project for an upper-division software engineering course. I built the menu system, implemented the Happy Hour feature using the Decorator design pattern, added input validation, and wrote a large share of the unit tests. I learned how the right design pattern keeps a feature clean and extensible, and how much a shared codebase leans on solid encapsulation and testing when several people contribute.

• Java
• OOP
• MVC

A Java application that models the data behind a music streaming service. Users browse a music store, add albums and songs to a personal library, build and manage playlists, rate songs, and track their listening history across individual user accounts. The design separates the source catalog from each user's personal collection and follows an MVC structure, written entirely by hand without GenAI. It was a two-person project for the same software engineering course, and I implemented the majority of the system, including the core library model, playlist management, user accounts, and play tracking. It sharpened how I model a real domain with clear, well-separated classes, and showed me how a strong class design keeps a system easy to reason about and extend.

• Java
• OOP
• MVC