Elections-and-Economies

Final project for the class "17.831 - Data and Politics", MIT Fall 2019.

Project Report
Project Presentation

Libraries required:

install.packages(c("tidyverse", "randomForest", "ROCit", "readxl", "TTR"))
Uses R version 3.6.1 (2019-07-05)

Data sources:

Unemployment rates:

https://www.bls.gov/web/laus.supp.toc.htm --> States and selected areas: Employment status of the civilian noninstitutional population, January 1976 to date, seasonally adjusted (ZIP)

State GDP data

https://apps.bea.gov/regional/downloadzip.cfm --> State GDP data.

Gubernational Elections:

https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/20408

Information sources:

1. https://www.investopedia.com/terms/c/civilian-labor-force.asp
2. https://towardsdatascience.com/random-forest-in-r-f66adf80ec9
3. https://machinelearningmastery.com/difference-test-validation-datasets/
4. https://en.wikipedia.org/wiki/Cross-validation_(statistics)#Leave-one-out_cross-validation
5. https://stats.stackexchange.com/questions/51416/k-fold-vs-monte-carlo-cross-validation
6. https://stats.stackexchange.com/questions/12412/how-do-you-generate-roc-curves-for-leave-one-out-cross-validation
7. https://stats.stackexchange.com/questions/90288/in-k-fold-cross-validation-does-the-training-subsample-include-test-set
8. https://towardsdatascience.com/why-linear-regression-is-not-suitable-for-binary-classification-c64457be8e28
9. https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76
10. https://elitedatascience.com/overfitting-in-machine-learning 11.https://stats.stackexchange.com/questions/61783/bias-and-variance-in-leave-one-out-vs-k-fold-cross-validation/252031#252031
11. https://en.wikipedia.org/wiki/List_of_recessions_in_the_United_States