https://docs.google.com/presentation/d/1zFRdjtbOxW_vMcC4ZCUpOz3KjfqbV778ngudOUq0ZsA/edit#slide=id.p

Here is a link to the document containing the information submitted for week 1 - [Week 1 Deliverables] (https://github.com/c2j185/Final_Project/blob/main/Team%202%20Week%201%20Deliverable.pdf)

Google Slides Presentation (https://docs.google.com/presentation/d/1zFRdjtbOxW_vMcC4ZCUpOz3KjfqbV778ngudOUq0ZsA/edit?usp=sharing)

Tableau Dashboard (https://c2j185.github.io/Final_Project)

Analyze large dataset of anonymous patient Covid-19 data provided by the CDC to determine metrics on severity of cases based on different criteria. (https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data-with-Ge/n8mc-b4w4)

1. Severity by State - Is there one part of the country that shows ‘more severity’ than the rest?
2. Severity by Gender - Is one gender showing more severe cases than the other?
3. Severity by Age Group - How do the age groups fair by severity?
4. Severity by Race - How do races compare when evaluating severity?

• Lisa Wagner - Machine Learning, Visualization, Website
• Stacey Conley - GITHUB, Visualization
• Ben Shelburn - Database

• Cleaned and customized dataframes for both Machine Learning and Visualization exported via flat file (.csv)
• Tables for Machine Learning and Visualization in pg Admin4
• Case severity risk analysis by State, Gender, and Age Group
• Tableau dashboard displaying Machine Learning findings and interactive components using the Visualization dataset
• Google Slides presentation detailing all portions of the analysis primarily using screenshot examples of the various stages

• Removed columns that would be be used during analysis and removed rows with missing values.
• Added "outcome" column.
• Used Random Forest Model with no data balancing for machine learning.

• No single state or part of the country was hit “harder” by severity than any other.
• The age group with the most cases was 18 - 49 years.
• The gender with the most cases was generally female.
• The race with the most cases was generally white.

• Try to find data on secondary causes of death that may not have been caused by COVID, but was put as the cause because the person was COVID positive at time of death.
• Now that vaccines have been available, include the vaccine data.

• Spend more time looking for a more complete dataset.
• Research API requirements and limitations.

Submitted 12-02-2021 by Stacey Conley, Ben Shelburn, Lisa K. Wagner