jrfarrer Goto Github PK

cis519_project

Final group project in Penn's Engineering school involved building a text classification model to allow content filtering on Facebook

coursematch_f17

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

coursematch_s18

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

dynamic_brand_equity

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

exploratory_data_analysis_lecture

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

fake_news

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

fake_news_app

Shiny mobile app that has users select whether headlines are real or fake (from Buzzfeed survey); connects to mysql DB on EC2

fnce611_hw1

Perpetuities, annuities, and yield to maturity on corporate bonds

fnce611_hw2

NPV, IRR, and capital budgeting

fnce611_hw3

Diversification, efficient portfolios, capital market line, and CAPM

fnce611_hw4

CAPM with Dell stock

jalpc_jekyll_theme

🍎Jalpc -- Jekyll Theme

jrfarrer.github.io

Personal webpage

jrftools

R package that contains personal functions

mktg712_hw2

mktg776_hw1

Forecasting customer retention using the beta-geometric model

mktg776_hw2

Modeling count data using the negative binomial distribution

mktg776_hw3

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

mktg776_hw4

Modeling choice data with the beta-binomial and Empirical Bayes

mktg776_hw5

Timing models such as the exponential-gamma to measure time to purchase

mktg776_hw6

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

mktg776_hw7

Latent-class count models using the NBD and Poisson

mktg776_p1

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

mktg776_p2

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

networkd3

D3 JavaScript Network Graphs from R

oidd898_hw2

A homework assignment from Seth Stephens-Davidowitz's class called Understanding Behavior with Big Data

stat701_hw1

Linear regression using different variable selection techniques

stat701_hw2

Classification using logistic regression and model selection criteria

stat701_hw3

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

stat701_hw4

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