https://github.com/mohd-faizy/08p_covid19_data_analysis_using_python

Data Analysis on COVID19 dataset, published by John Hopkins University
https://github.com/mohd-faizy/08p_covid19_data_analysis_using_python

covid19-data data-analysis data-analysis-python data-visualization happiness-report-dataset pandas python seaborn statistics

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Data Analysis on COVID19 dataset, published by John Hopkins University

• Host: GitHub
• URL: https://github.com/mohd-faizy/08p_covid19_data_analysis_using_python
• Owner: mohd-faizy
• Created: 2020-08-18T16:51:23.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-12-31T20:04:54.000Z (about 4 years ago)
• Last Synced: 2024-11-13T09:47:16.693Z (3 months ago)
• Topics: covid19-data, data-analysis, data-analysis-python, data-visualization, happiness-report-dataset, pandas, python, seaborn, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 12.6 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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# __COVID19 Data Analysis Using Python__



  



 

In this Project we are going to work with COVID19 dataset, published by John Hopkins University, which consist of the data related to cumulative number of confirmed cases, per day, in each Country. Also we have __another dataset__ consist of various life factors, scored by the people living in each country around the globe.  We are going to merge these two datasets to see if there is any relationship between the spread of the the virus in a country and how happy people are, living in that country.  


## DataSet

1. [COVID19 dataset](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) published by John Hopkins University.

2. [World_Happiness Report](https://github.com/mohd-faizy/08P_COVID19_Data_Analysis_Using_Python/blob/master/world_happiness_report_2019.csv)

3. [CSSEGISandData](https://github.com/CSSEGISandData)

## Objectives

- Learn the steps, needed to be taken to prepare our data sources for an analysis.

- Learn how to look at our data to find a good measure to establish our analysis based upon. 

- Learn to visualize the result of our analysis. 

### :heavy_check_mark: Task 1: Introduction

- Understand the purpose of the project, the datasets that will be used, and the question we will answer with our analysis.

### :heavy_check_mark: Task 2: Importing COVID19 dataset

- Import COVID19 dataset and prepare it for the analysis by dropping columns and aggregating rows.

### :heavy_check_mark: Task 3: Finding a good Measure

- Decide on and calculate a good measure for our analysis.

### :heavy_check_mark: Task 4: Importing World happiness report dataset

- Import World happiness report dataset, dropping useless columns and Merge it with COVID19 dataset to find correlations among our data.

### :heavy_check_mark: Task 5: Visualizing the results

- Visualize our results using Seaborn.

### Connect with me:

[][twitter]

[][linkedin]

[][StackExchange AI]

[twitter]: https://twitter.com/F4izy

[linkedin]: https://www.linkedin.com/in/faizy-mohd-836573122/

[StackExchange AI]: https://ai.stackexchange.com/users/36737/cypher

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