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Use of Interpretable Machine Learning for Analyzing Socio-economic disparities and COVID-19 in the USA

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Socio-economic disparities and COVID-19 in the USA

Ayan Paul, Philipp Englert and Melinda Varga

This repository contains all the codes necessary for collection, curation and analysis of the results in this paper. The scripts for making the plots and the plots are also included

Directory Struture:

.
├── Maps
│   ├── data
│   │   └── US
│   │       ├── confirmed.json
│   │       ├── deaths.json
│   │       ├── Density.json
│   │       ├── Employed.json
│   │       ├── Income.json
│   │       ├── Labour.json
│   │       ├── MeanCommute.json
│   │       ├── Non-White.json
│   │       ├── Poverty.json
│   │       ├── SeniorCitizen.json
│   │       ├── Transit.json
│   │       ├── Unemployment.json
│   │       └── Uninsured.json
│   ├── js
│   │   ├── maps-US.js
│   │   └── us-all-all-highres.js
│   └── maps-US.html
├── Notebooks
│   ├── mapdata-US.ipynb
│   └── The-Unfair-World-USA.ipynb
├── plots
│   ├── EC_Corr.png
│   ├── EC_CR.png
│   ├── EC_DR.png
│   ├── SS_Corr.png
│   ├── SS_CR.png
│   ├── SS_DR.png
│   ├── USA_Corr.png
│   ├── USA_CR.png
│   ├── USA_DR.png
│   ├── WC_Corr.png
│   ├── WC_CR.png
│   └── WC_DR.png
├── LICENSE
└── README.md     

used package versions:
numpy version: 1.17.2
pandas version: 1.0.5
sklearn version: 0.21.3
xgboost version: 1.0.2
shap version: 0.34.0
matplotlib version: 2.2.3
seaborn version: 0.9.0

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