How sifat192/covid19-analysis works
This repository is a notebook-based analysis of COVID-19 infection trends and their relationship to World Happiness indicators. The documented workflow cleans and aggregates infection data, computes country-level maximum infection rates, combines those results with happiness data, then analyzes and visualizes relationships. The notebook itself was not sampled, so the internal stages and their ordering are based on the README rather than inspected implementation; no unverified code-level details are inferred.
Main entry points: covid19 data analysis notebook.ipynb Clean and Aggregate · covid19_Confirmed_dataset.csv COVID Dataset · worldwide_happiness_report.csv Happiness Dataset
Components 10
COVID Data
- COVID Dataset
Datasets/covid19_Confirmed_dataset.csv - Clean and Aggregate
covid19 data analysis notebook.ipynb - Infection Growth
covid19 data analysis notebook.ipynb - Maximum Infection Rate
covid19 data analysis notebook.ipynb
Happiness Data
- Happiness Dataset
Datasets/worldwide_happiness_report.csv - Process Indicators
covid19 data analysis notebook.ipynb
Analysis
- Country Merge
covid19 data analysis notebook.ipynb - Correlation Analysis
covid19 data analysis notebook.ipynb - Relationship Plots
covid19 data analysis notebook.ipynb
Other components
- Analyst
Connections 9
- Analyst → Clean and Aggregate: runs analysis
- COVID Dataset → Clean and Aggregate: loads and cleans
README.md - Clean and Aggregate → Infection Growth: aggregates for
README.md - Infection Growth → Maximum Infection Rate: computes maxima
README.md - Happiness Dataset → Process Indicators: processes
README.md - Maximum Infection Rate → Country Merge: merges by country
README.md - Process Indicators → Country Merge: merges by country
README.md - Country Merge → Correlation Analysis: analyzes correlation
README.md - Correlation Analysis → Relationship Plots: visualizes relationships
README.md