Problem Statement:
How effective has each U.S. state been at treating patients diagnosed with the coronavirus? This question is important because each state has different resources and receives varying levels of government funding. If more people in one state recover from the illness compared to the people living in another state, studying the variables that might have led to this has the potential to increase the nationwide recovery rate. There are many variables that would contribute to state recovery rate, like population density, number of hospitals, number of resources, government funding, geographical features, average diet, average level of physical exercise, and so on. It will be important to study these factors. However, the first step is to study the number of cases and deaths by state over time.
Link to download:
https://github.com/paulmarinos/covid-19/blob/master/covid-states.py
Objectives:
- Analyze the number of COVID-19 cases and deaths by U.S. state over time
- Calculate COVID-19 state mortality rate
- Create a tool that will quickly visualize the data using tables and charts
- Allow user to search by either state or date and sort data by column
- Allow user to plot the data and fit a linear regression model
- Calculate the R-squared value
Libraries used:
- pandas for data preparation
- numpy for data manipulation
- matplotlib for data visualization
- sklearn for statistical modeling
R-squared value meaning:
- The r-squared value, or correlation of determination, explains how closely correlated the dependent value (cases) is with the independent value (deaths). As the number of cases increases, the number of deaths to the virus should increase as well. If the cases and deaths of a state do not have a linear relationship then the way the state is treating patients infected with the disease should be studied in order to help explain why, so that they can modify their treatment practices if its recovery rate is subpar or so that other states can see if they can emulate their treatment practices if the state has a higher recovery rate / mortality rate.
Datasource (updated daily):
Screenshots (Viewing Data):
search by state to view date, cases, deaths, and mortality rate
search by date to view state cases, deaths, and mortality rate up to a given day
Sort by Mortality Rate
Screenshots (Plotting and Performing Linear Regression):
Create Scatterplot and perform Linear Regression
Calculate R-Squared Value
Screenshots of Python Code:
Header
Import Libraries
Use Pandas for Data Preparation
Create Functions to Filter Data Frame
Create function to give option to sort by column
Plot Linear Regression
Create Options Menu
Call Main Function to Start the Program
