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

Diversification, efficient portfolios, capital market line, and CAPM

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

Linear regression using different variable selection techniques

Latent-class count models using the NBD and Poisson

Classification using logistic regression and model selection criteria

As a part of Business Analytics specialization from Wharton School, University of Pennsylvania, worked on the capstone project.

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

Brand concentration using count models; means and zeroes and method of moments estimation

Basketball Prediction System built for the 2025 Wharton Data Science competition

Modeling choice data with the beta-binomial and Empirical Bayes

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

Modeling count data using the negative binomial distribution

This project was developed as part of the Wharton High School Data Science Competition(https://wsb.wharton.upenn.edu/wharton-data-competition/), organized by the Wharton Sports Analytics and Business Initiative. The competition provided teams with simulated soccer league data to develop predictive models for playoff outcomes.

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

CAPM with Dell stock

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

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

Forecasting customer retention using the beta-geometric model