Socioeconomic Indicators Analysis Project

Welcome to the Socioeconomic Indicators Analysis Project! This repository contains code and resources for analyzing and visualizing the distribution of key socioeconomic indicators for various countries.

Business Understanding

The objective of this project is to analyze and visualize key socioeconomic indicators for various countries, focusing on access to electricity, GDP growth, and unemployment rates.

Identify Priority Markets: Determine countries with a high demand for improved access to electricity and a positive economic outlook (high GDP growth)
Evaluate Economic Viability: Analyze unemployment rates in different regions to gauge the labor force's stability and the potential for workforce availability
Understand Regional Dynamics: Examine how different regions' socioeconomic indicators vary to uncover potential trends or correlations
Provide Data-Driven Recommendations: Present actionable insights and visualizations that help make informed decisions

Data Understanding

The data project aims to explore various socioeconomic indicators across different countries. The dataset includes information on a wide range of indicators, such as "Access to electricity (% of population)," "GDP growth (annual %)," "Unemployment, total (% of total labor force) (modeled ILO estimate)," and more. The data is sourced from reputable international organizations and provides valuable insights into global economic and social trends.

Data Source:

It was provided by 'World Bank'. The data is collected from reliable sources, such as international organizations, government agencies, and research institutions. These sources ensure data accuracy and credibility, enabling robust analysis and informed decision-making.

Data Format:

The dataset is structured as a collection of rows and columns in tabular format, where each row corresponds to a specific country and each column represents an indicator. Each indicator column contains numerical values representing the respective indicator's measurement for each country.

Variables:

The variables in the dataset include:

Country Name: Name of the country for which the data is recorded. Country Code: Unique code assigned to each country. Indicator Name: Name of the socioeconomic indicator being measured. Indicator Code: Unique code assigned to each indicator. Year: Year in which the data was recorded. Value: Numerical value representing the measurement of the indicator for the given year and country.

Data Quality:

The dataset has undergone preprocessing to ensure data quality. Missing values, inconsistencies, and outliers have been addressed to provide accurate and reliable results. Additionally, metadata files provide context for indicator names and country information, enhancing data interpretation.

Data Exploration:

The data will be explored through various visualizations, including histograms, bar plots, and regional analyses. These visualizations will provide insights into distribution patterns, trends, and relationships between indicators and regions.

Key Questions:

How do different countries perform in terms of various socioeconomic indicators? Are there any significant trends or patterns in the data across different years? What are the disparities in indicators among different income groups and regions? How do countries compare in terms of unemployment, GDP growth, literacy rates, and other key indicators? Expected Outcomes: By thoroughly understanding the dataset and conducting exploratory analysis, we aim to gain insights into global economic and social trends, identify correlations between indicators, and understand the factors influencing countries' performances. These insights will lay the foundation for informed decision-making and further analysis to address societal challenges and promote inclusive development.

Technical Details

The project is implemented in Python and leverages popular data analysis and visualization libraries, including pandas, matplotlib, seaborn, and plotly. The codebase is organized into classes and functions to ensure modularity and reusability.

Dependencies

The project requires the following dependencies:

pandas matplotlib seaborn plotly numpy

Results

The project provides valuable insights into access to electricity, GDP growth, and unemployment rates for different countries and regions. Visualizations highlight trends, correlations, and potential priority markets for the energy company's expansion.

Conclusion

By leveraging data analysis and visualization, this project assists the energy company in making informed decisions about their expansion plans. It showcases the power of data-driven recommendations in aligning business goals with socioeconomic factors.