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Based on research published in the International Journal of Data Science, machine learning algorithms may be used to produce accurate predictions regarding population trends. The study shows that the best available algorithms trained on historical data outperform traditional demographic modeling based on census data.
This study used different machine learning algorithms to forecast the population such as Extreme gradient boosting, CatBoost, linear and ridge regression, Holt-Winters, exponential, autoregressive integrated moving average (ARIMA), and prophet prediction model. Between 1960 and 2017, models were trained using 1595 distinct demographic variables from 262 different nations.
Machine learning algorithms outperformed the demographic model, according to the findings. When the results of other algorithms were examined, the extreme gradient boosting model was the most successful. Furthermore, the entire population of Turkey in 2017 was approximated using pre-trained machine learning algorithms, and the result projected using the Cohort component technique was compared.
Extreme gradient boosting, CatBoost, linear regression, ridge regression, Holt-Winters, exponential, autoregressive integrated moving average (ARIMA), and prophet prediction model were compared by Fatih Veli ahinarslan, Ahmet Tezcan Tekin, and Ferhan ebi of Istanbul Technical University’s Department of Management Engineering in Istanbul, Turkey. They used 1595 distinct demographic statistics from 262 nations between 1960 and 2017 to train the algorithms. The indicators are age and gender distribution, labor force, education, birthplace, birth and death rates, and migration data.
The advantage of the algorithmic technique over traditional modeling was demonstrated in their presentation of predicting Turkey’s population for 2017. Understanding population dynamics and anticipating how a population will change in the future is an essential component of policymaking and planning in healthcare, education, housing, transportation, and infrastructure. Ten-year census cycles are helpful, but they do not provide a fine-grained picture of a changing population, especially in light of changes in life expectancy, migration, war, political upheaval, and pandemics, all of which can cause a population’s character to change dramatically over a much shorter timeframe.
The researchers claim that machine learning techniques, namely ensemble regression models, can provide a “better estimate” of a country’s future population. They may do so because they can limit the number of components that would otherwise make estimation difficult and analyze any ambiguities in the demographic data.
The researchers concluded that Machine learning algorithms for population prediction would be critical to national needs planning and pave the path for more consistent social, economic, and environmental decisions.