The project aims to analyze the GDP per capita PPP (Purchasing Power Parity) of countries 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 different countries.
The data will be cleaned and preprocessed before performing any analysis. The data will be stored in a SQL database and will be accessed using SQL queries for data manipulation and analysis. Additionally, data visualization will be done using Power BI to create interactive and informative dashboards.
The data will be explored through various visualization techniques such as histograms, scatter plots, and line charts. The project will use statistical methods to identify patterns and trends in the data.
In the next phase, the project will use 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.
The project will also focus on identifying the most significant factors that contribute to the growth or decline in GDP per capita PPP in different countries. The findings of the project will be useful for policymakers, researchers, and other stakeholders in the field of economics and development.
The project will be implemented in Python and will use libraries such as pandas, numpy, matplotlib, seaborn and scikit-learn for data cleaning, preprocessing, visualization and modeling. The SQL database and Power BI will be used for data storage, manipulation and visualization. The project will be developed on GitHub and will be open-source, allowing for collaboration and contributions from other data analysts and developers.