Writings on the motivation and inspiration of my projects as well as the tech stacks behind them, collected in chronological order.
This article discusses the process of ingesting and cleaning data to train Support Vector and Random Forest regression models on San Diego AirBnB data to predict the nightly price of a listing.
Utilizing GitLab CI, AWS, and Docker, I automated the building, testing, containerization and deployment of a React application to the cloud.
This project compared the effectiveness of six different machine learning techniques in predicting reservoir storage value based off of historical climate and reservoir data. Top models recorded up to 99.8% effectiveness in hindcast reporting.
Confronted with the issue of scamming and malicious activities in online marketplace communities on Discord, my partner and I set out to build a solution. 500+ users and $100k in reported transcations later, we have Vouchify.
I utilized Flask to create a custom API that leverages congressional trading information and STOCK Act data to predict high potential stocks. This data analysis algorithm leverages 35,000+ reported public transactions to enable informed investment decisions and empowers users to stay ahead of the game.
This project, named YELL SOMETHING, utilized modern web development integrations and techniques including NextJS, TypeScript, Prisma, Zod, tRPC, NextAuth.js, and Tailwind to create a simple posting service.