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dobriban / Topics-in-deep-learning
Materials for class on topics in deep learning (STAT 991, UPenn/Wharton)
Language:HTML 94usefulness / dependency-tree-diff-action
Github Action wrapper for the Jake Wharton's dependency-tree-diff tool
Language:Shell 10Github Action wrapper for the Jake Wharton's Diffuse library
Language:Python 10jrfarrer / 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
Language:R 4Final 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
Language:HTML 3A homework assignment from Seth Stephens-Davidowitz's class called Understanding Behavior with Big Data
Language:TeX 2Builds Google Sheet (650+ visits) with course and instructor evaluations and clearing prices distributed from the Wharton Analytics Club; visualization to help select courses
Language:HTML 0A lecture on the basics of exploratory data analysis using tidyverse as a TA for Wharton's Statistics Department's STAT701 - Modern Data Mining.
Language:HTML 0Applying NBD count models to examine the behavior of Wharton MBA students on the messaging platform GroupMe
Language:HTML 0Implement timing model that will predict Dish Network’s subscriber acquisition in 2017
Language:HTML 0Builds Google Sheet (600+ visits) with course and instructor evaluations and clearing prices distributed from the Wharton Analytics Club; visualization to help select courses
Language:HTMLAssisted Wharton professors with data analysis for a paper on the changing of brand value over time
Language:HTMLPerpetuities, annuities, and yield to maturity on corporate bonds
Diversification, efficient portfolios, capital market line, and CAPM
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Brand concentration using count models; means and zeroes and method of moments estimation
Timing models such as the exponential-gamma to measure time to purchase
Discounted expected residual lifetime value using the Beta-discrete-Weibull; integrated models such as BG/BB
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Classification using logistic regression and model selection criteria
Language:HTMLRegularization using LASSO and ridge regression; cross-validation for parameter selection
Language:HTMLText classification using logistic regression, SVM, and random forest; PCA
Language:HTMLBuilding a model to predict whether or not a patient would be readmitted within 30 days after diabetic hospitalization
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