Deployed bird classification webapp using Deep Learning, Docker, and Streamlit. Users can go onto the webapp and either upload their own images of birds or select from a set of images to feed through a Deep Learning model and display a prediction.
Deployed webapp to help data science job seekers find jobs relevant to their skill set. Utilized Python, Docker, AWS, Scikit-Learn, and Streamlit to create an interactive webapp aimed at helping job seekers with the messy process of finding a job that fits them.
DSPy: The framework for programming—not prompting—foundation models
Deployed event fraud prediction utilizing Python, Docker, NLP, Sklearn, and AWS that takes in live events and in real-time, predicts whether they are fraudulent events with almost perfect accuracy.
EDA and basic machine learning implementation as well as simple evaluation in R. This is meant to be an introductory level learning resource for people to learn about predictive modeling in R.