Statistical and Machine Learning Analysis of Impact of Population and Gender Effect in GDP of Bangladesh: A Case Study
Автор: Rayhan Ahmed, Ashfaq Ali Shafin
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 1 Vol. 12, 2020 года.
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Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth that records for its number of people. A balanced participation ratio of both males and females in the industry by ensuring skilled and technical education for all provides a stable economic development in a country. Population and Gender impact on GDP prices in Bangladesh were investigated in this study. To address the effect of gender factors in GDP prices, we considered the following parameters: year, combined population, male population, and female population. Based on these parameters, the global domestic product-current prices of Bangladesh were analyzed. For the predictive analysis, we have used various machine learning algorithms to make prediction and visualization of the predicted output. A quantitative analysis was also performed to examine the correlation among different gender factors with the growth of GDP. Based on analysis and study results, we can say that the machine learning approach could be applied efficiently in numerous applications of GDP forecasting.
GDP, Population, Bangladesh, Machine Learning, Linear Regression, Polynomial Regression, SVR
Короткий адрес: https://sciup.org/15017100
IDR: 15017100 | DOI: 10.5815/ijitcs.2020.01.04
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