Supervised Learning in R: Using Deep Learning and Machine Learning Techniques to Predict Law School Admissions.

For this data science final project I trained, tuned, and tested neural networks and random forest models on a dataset of over 400,000 law school admissions observations. The response variable that I was interested in was was admissions outcome — e.g. whether the applicant was accepted, rejected, or waitlisted. I used over 30 predictor variables, the most influential being LSAT Score, High School GPA, and Acceptance Rate of School. Download the report here.

Predictive Modeling in R: Using NBA Rookie Performance to Predict Career Measures with Both Regression and Classification Modeling Techniques.

For this data science final project I used rookie years statistics to predict career measures. The response variables that I was interested in were Career VORP (overall career performance) and whether a player stayed in the league for five years or not (a binary TRUE or FALSE outcome). These response variables necessitated that I use both regression and classification modeling techniques, as well as set validation to evaluate the 40+ models in the final set. Download the report here.

Data Exploration in R: Examining Positional Shot Data for the 2014 World Cup to Understand Goal Likelihood and Shot Direction Relative to Shot Position.

For this data science final project I performed an exploratory data analysis on a positional soccer dataset. The dataset contained information on each shot from the 2012 Champions League competition. All the visuals were coded in R and the data cleaning and analysis was also all done in R. Download the report here.

Data Journalism: The Predictive Power of Dillo Day

A dive into the predictive power of Northwestern’s largest music festival – Dillo Day. I used Google Trends search data to analyze artists career paths before and after performing at Dillo Day. The article was published in The North by Northwestern Magazine. Access the online version here.

Data Journalism: Go U Northwestern, I Forget This Line

A visual analysis of Northwestern’s fight song, broken down by year (Freshman, Sophomore, etc…). I got nearly 100 participants to recite the fight song for me and evaluated their correctness on a word-by-word basis to arrive at this graph. The article was published in the North by Northwestern Magazine. Access the online version here.

Data Visualization and Dashboarding in R Shiny: Analyzing NBA Player Performance by Home State

As a final project for my Statistics 302 – Data Visualization class, I created this interactive app, allowing users to explore NBA player performance by home state. The app was created using R and Shiny, an interactive graphics package. Access the app here.