This project aims to analyze the GDP per capita PPP (Purchasing Power Parity) of countries in order to identify patterns and trends in the data. The main focus of the analysis will be to study the correlation between growing and declining GDP per capita PPP in approximately 186 countries.
The findings of this project will be useful for policymakers, researchers, and other stakeholders in the field of economics and development.
The project starts by scraping data from Wikipedia, and merges the output into a table of all countries what you will find in this file: http://bit.ly/3yxmaAi.
The project is implemented in Python, using libraries such as pandas, numpy, matplotlib, and seaborn for data cleaning, preprocessing, visualization, and modeling: http://bit.ly/3T5cPcx.
The project also utilizes statistical methods to identify patterns and trends in the data, and uses IBM Cognos for data manipulation and visualization. All findings are documented in the report: http://bit.ly/4017OnM.
This project is developed on GitHub and is open-source, allowing for collaboration and contributions from other data analysts and developers.
In the next phase, the project will utilize machine learning techniques such as linear regression to model the relationship between GDP per capita PPP and other economic indicators such as GDP growth rate, inflation rate, and unemployment rate.