The dataset for this project contains records of the global development and prosperity index for the year 2023
Data cleaning, analysis and visualization was done using python. The analysis provides answers to some important questions and to get an understanding of the dataset.
Data Structure:
Columns in the dataset include; Country Code, Country, Average Score, Safety Security, Personnel Freedom, Governance, Social Capital, Investment Environment, Enterprise Conditions, Market Access Infrastructure, Economic Quality, Living Conditions, Health, Education, Natural Environment.
The necessary python libraries needed to carry out this analysis was imported into the python IDLE (Jupyter Notebook), And the dataset was loaded in to begin analysis.
Total numbers of column and rows present in the dataset shows 167 rows and 14 columns.
10 random samples of the dataset to see what the dataset looks like.
Data Cleaning
Data cleaning was done using the python pandas library in order to “clean “ the dataset and prepare it for analysis.
•Checking for missing values in the dataset
The image above shows the dataset had no missing values
The image above shows there were no duplicates in the dataset.
## Data Analysis and Exploration
1)Top Ten Countries By Average Score Of The Global ProsperityIndex:
•The visualization shows the top ten countries ranked by their average scores on the Global Prosperity Index. These countries demonstrate strong performances across various metrics such as governance, education, health, and economic quality. The high scores indicate a robust and balanced approach to fostering prosperity and well-being for their citizens, reflecting effective policies and a favorable socio-economic environment.
**2)Bottom Ten Countries by Average score:
•This list and visualization highlights areas where these countries may need to focus their efforts to improve their overall scores, contributing to better quality of life and development outcomes for their citizens. It serves as a valuable tool for policymakers, researchers, and stakeholders interested in international development and comparative analysis.
*3)Areas Scored Highest by the top Ten countries: *
This list and Visualization titled “Areas Scored Highest by the Top Ten Countries” illustrates the top-performing metrics for the ten countries with the highest average scores. These metrics encompass various dimensions of national success, including safety, personel freedom, governance, social capital, economic quality, and more.
4)Areas of Improvement for the Bottom Ten Countries:
This list and chart titled “Areas Scored Highest by the Top Ten Countries” illustrates the top-performing metrics for the ten countries with the highest average scores. These metrics encompass various dimensions of national success, including safety, personnel freedom, governance, social capital, economic quality, and more.
5)Relationship Between Governance and Living Conditions:
The correlation of 0.71 between governance and living conditions underscores the importance of strong governance as a key driver for improving living conditions. This relationship suggests that efforts to enhance governance structures can have a significant positive impact on the quality of life of a country’s population. Policymakers and development organizations can use this insight to prioritize governance reforms as a strategy to elevate living conditions.
