World Population Analysis Using Machine Learning

Abstract

Population analysis is crucial for understanding societal
trends, planning resources, and forecasting future needs.
This paper explores the application of machine learning
techniques to analyze world population trends. We begin
by collecting and preprocessing data from various
sources, including census data, surveys, and other
demographic indicators. We then use machine learning
models such as regression, clustering, and forecasting
algorithms to gain insights into population trends. Our
analysis focuses on several key aspects, including
population growth rates, age distribution, urbanization
patterns, and migration trends. We use regression models
to understand the factors influencing population growth
and demographic changes. Clustering techniques help
identify distinct population groups based on demographic
characteristics. Furthermore, we employ forecasting
algorithms to predict future population trends based on
historical data. By analyzing and visualizing the results,
we can gain valuable insights into global population
dynamics, which can inform policy-making and resource
allocation decisions. Overall, this study demonstrates the
effectiveness of machine learning in analyzing complex
population data and provides valuable insights into global
demographic trends