Jordan Farrer's repositories
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
oidd898_hw2
A homework assignment from Seth Stephens-Davidowitz's class called Understanding Behavior with Big Data
cis519_project
Final group project in Penn's Engineering school involved building a text classification model to allow content filtering on Facebook
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
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.
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
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
dynamic_brand_equity
Assisted Wharton professors with data analysis for a paper on the changing of brand value over time
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
jrfarrer.github.io
Personal webpage
jrfTools
R package that contains personal functions
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
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
stat701_miniproject
Building a model to predict whether or not a patient would be readmitted within 30 days after diabetic hospitalization
sync_canvas
A simple solution to sync course files on Canvas to a local machine