## Data Mining

This is a team project that uses good data grooming and machine learning methods to predict when a hospital patient might be readmitted. I demonstrate data cleaning, supervised learning, regularization, and model selection methods to make decent predictions. **Click the image to learn more.**

## Statistical Modeling

This project uses various statistical techniques to estimate from data the generating probability distribution. I use maximum likelihood estimation, hypothesis testing, and Bayes factors among others to analyze data. **Click the image to learn more.**

## Machine Learning Interpretability

This project focuses on developing and finding the limitations of interpretability techniques in machine learning. I show that representational similarity analysis, a statistical technique for comparing model representations, depends heavily on the measure of similarity it uses. **Click the image to learn more.**

# Unsupervised Learning

This project uses unsupervised learning techniques to learn a generative model for crime statistics. I demonstrate data cleaning, Python coding, generative modeling methods, and graphic visualization techniques. **Click the image to learn more.**