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Hi! I'm Brandon, and I have a professional background in both working and teaching data analytics. In recent years, I've broadened my skillset to include iOS development. I love being able to carry programs I'm writing in my pocket!

I am currently interested in merging data with mobile apps, creating ML models that run on the phone and physics simulations that represent data.

Feel free to reach out to me over LinkedIn or check out articles I've written on topics like these at Medium.

This app allows for users to experiment with different health measurements, such as blood glucose levels and BMI. Selecting different values for these measurements filters patient data, visualizing similar matches. These values are then used to generate a prediction as to whether or not a patient would have a positive diabetes diagnosis.

1. How ML models be adapted to run on the iPhone
   • Apple's Core ML tools library is an excellent way to transition Python models to iOS
2. Intuitive ways of interacting with ML models on mobile
   • Sliders with min/max ranges based on data work great for feature selection
3. How a mobile app can make discovering relationships with the data and machine learning model easier for both technical and non-technical users
   • The Charts library dynamically updating with user input goes a long way to demonstrate how features affect classifications

• CoreML, Charts, SwiftUI, Python, scikit-learn

I also have an article diving into the specifics of this app here

An interactive setting to test newtonian physics. Handles various shapes and text, and includes a paint mode for creating illustrations from shapes.

1. How SpriteKit can be utilized to simulate physics and shapes
2. How to customize interface views to meet design needs
3. Computational tricks to match colors and integrate into UI design

• SwiftUI, SpriteKit