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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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backwards-selectiongssrandom-forest

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.

backwards-selectiongssrandom-forest