arrudafranco / Homework-7

Basic machine learning methods. One is a random forest model used to choose among categorical variables from the GSS to predict attitudes towards racist college professors, the other is a backwards selection algorithm to choose variables to predict student debt. Produced for the course "Computation for the Social Sciences", offered during the Fall of 2020 at the University of Chicago.

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hw07

Gustavo Arruda

This repository is part of a University of Chicago course called "Computation for the Social Sciences" taught in the fall of 2020. It contains models for median college student debt data and also data with a subset of variables concerning attitudes towards racist professors from the General Social Survey, both from the US.

  • scorecard.Rmd and gss-colrac.Rmd are Markdown files that renders written analysis of the data.
  • scorecard.md contains charts and details about linear models of student debt data in the US.
  • gss-colrac.md contains charts and details about classification models of attitutes towards racist professors in the US.

Used Libraries:

  • To run the code in this repository, the libraries used were:
  • library(tidyverse)
  • library(rcfss)
  • library(leaps)
  • library(caret)
  • library(ggplot2)
  • library(knitr)
  • library(broom)
  • library(randomForest)
  • library(partykit)

About

Basic machine learning methods. One is a random forest model used to choose among categorical variables from the GSS to predict attitudes towards racist college professors, the other is a backwards selection algorithm to choose variables to predict student debt. Produced for the course "Computation for the Social Sciences", offered during the Fall of 2020 at the University of Chicago.