Materials for class on topics in deep learning (STAT 991, UPenn/Wharton)

Final project using a survey commissioned by Buzzfeed to identify the factors that best predict whether someone can correctly distinguish between accurate and inaccurate news headlines

Classification using logistic regression and model selection criteria

Latent-class count models using the NBD and Poisson

Linear regression using different variable selection techniques

Applying NBD count models to examine the behavior of Wharton MBA students on the messaging platform GroupMe

Regularization using LASSO and ridge regression; cross-validation for parameter selection

A lecture on the basics of exploratory data analysis using tidyverse as a TA for Wharton's Statistics Department's STAT701 - Modern Data Mining.

Builds Google Sheet (600+ visits) with course and instructor evaluations and clearing prices distributed from the Wharton Analytics Club; visualization to help select courses

Assisted Wharton professors with data analysis for a paper on the changing of brand value over time

CAPM with Dell stock

Implement timing model that will predict Dish Network’s subscriber acquisition in 2017

Builds Google Sheet (650+ visits) with course and instructor evaluations and clearing prices distributed from the Wharton Analytics Club; visualization to help select courses

Building a model to predict whether or not a patient would be readmitted within 30 days after diabetic hospitalization

Text classification using logistic regression, SVM, and random forest; PCA

Discounted expected residual lifetime value using the Beta-discrete-Weibull; integrated models such as BG/BB