Predicting Populations: Modeling Demographic Predictions for Nations Around the World Using Population Pyramids and Demographic Transition Models

In this research project, I used multiple machine learning models(neural network and linear regression) in order to predict key demographic statistics over the next 5 years for each nation.

Examining and analyzing demographic statistics are a key foundation for analyzing the makeup of a population for a given nation. Some of the most important population demographics include total population, crude birth rate(CBR - births/1000 people), crude death rate(CDR - deaths/1000 people), and populations of different age groups and genders. These help provide a basis into understanding the overall population growth and stage of development of a particular country, which can ultimately be used to shape economic, political, and social policies for different nations around the world. In this research project, I used multiple machine learning models(neural network and linear regression) in order to predict key demographic statistics over the next 5 years for each nation. These models yielded accurate results, as most sets of predictions had accuracy scores above 0.85. Additionally, using advanced visualization techniques, I modeled these predictions using popular human geography models such as population pyramids and demographic transition models. I found that for more developed nations such as countries in Western Europe and East Asia, the population of older individuals is going to rise significantly over the coming years. For less developed nations such as those in Africa, the overall population is going to skyrocket, and the proportion of young adults who will enter the labor force will significantly surge. In terms of real-world implications, these results could lead to more developed nations starting to spend more on social security and less developed nations starting to implement policies that limit population growth.

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