holy-angel-university/population-figures-analysis

This project analyzes global population trends using World Bank data. Findings show population growth varies by region, with faster growth in Sub-Saharan Africa and South Asia. Machine learning models predict future trends, with Logistic Regression excelling in identifying population growth. # Population figures for countries, regions (e.g. Asia) and the world Dataset URL = datahub.io Global Population Analysis This project studies how populations have changed over time across different countries and regions. Using a large dataset with population information from many countries over several decades, we aim to identify trends in population growth, understand the impact of economic and social factors, and explore how these trends differ around the world. ## Dataset Description The data comes from the World Bank and includes population numbers for many countries every year from 1960 to today. Key pieces of information in the dataset are “Country Name,” “Country Code,” “Year,” and “Population.” This data lets us explore how populations in different parts of the world have grown or changed over time. ## Summary of Findings Our findings show that population trends differ greatly by region. In areas like Sub-Saharan Africa and South Asia, populations are growing quickly, while in more developed areas like Europe, growth is slower or even declining. Generally, countries with stronger economies tend to have slower growth due to factors like urbanization and lower birth rates. In contrast, countries with developing economies often experience faster growth, largely driven by higher birth rates. ## Data Preprocessing Necessary import libraries Preferences During data processing, the dataset was loaded using pd.read_csv and went through multiple preparation procedures to ensure it was ready for analysis. Initial processes, including population.head(), population.tail(), and population.info(), helped identify data types and confirm consistency across rows and columns. Summary statistics of the "Year" column provided a time range overview, and missing values were checked with population.isnull().sum() to detect …