Adeleye-Emmanuel/Covid-19_Economic_Impact_Analysis_in_Nigeria
Domain:
healthcaresocioeconomicCreator:
Ade
Host:
An exploratory study examining the effects of COVID-19 in Nigeria, analyzing its impact on GDP, covid-19 community vulnerability responses, population dynamics, and crude oil price trends during the pandemic across all country states
# DATA ANALYSIS ON IMAPCT OF COVID-19 PANDEMIC IN NIGERIA
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus, and it has affected major parts of the world. Nigeria, a West-African country, has also been affected by the COVID-19 pandemic after recording its first case on 27th February 2020.
Nigeria is a country with 37 states - Federal Capital Territory included- and a fast-growing economic environment with about 200 million citizens. COVID-19 has affected several country activities as the country steadily progressed from its first case to shutting down major airports, state-wide lockdown, curfews, and reviving its economy.
In this project data science & analytics skills are used to collect data, explore the data, perform analysis, create visualizations, and generate insights.
# Project Steps
In the course of the analysis the following steps were taken before and during the analysis;
1). Gathered data from three different sources; ncdc website, and john hopkins github repoitory and covid_external_data repository
* Web scrapped the dataframe using pd.read_html()
* Web scrapped the data from the John Hopkins repository
* Downloaded external data from Covid_external Repository
2). NCDC DATA
* Basic insights derivation form .info(), .describe() and viewed the complete data
* Plotted a bar charts of all the states affected to number of confirmed cases, no of discharged cases and number of deaths
* Performed correlation analysis using scatterplots and relplots
* Observed the distribution of confirmed cases
3). John Hopkins Data
* There are three subdivisions of the data; the nuber of confirmed cases, discharged cases and death cases.
* Extracted and combined into a single dataframe
* Basic insights derivation form .info(), .describe() and data.head()
* Used the pd.melt() function to convert all the confirmed, discharged and death date features into one date feature mapping to the date value
* Merged the three frames in …