TY - GEN
T1 - Exploratory Data Analytics of Total Population Over Fertility Rate in South Korea
AU - Ganesan, Anusha
AU - Paul, Anand
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The Republic of Korea is experiencing a demographic crisis with low birth rates and aging population. As the present circumstance represents a developing danger to the supportability of its economy, schooling, accounts, and protection, there is a critical requirement for definite and comprehensive activity. South Korea is one among the world's quickest maturing nations. There might be a crucial impact forecasted if the country does not mitigate this growing jeopardy on the population. In this paper, we designed a prediction model using the machine learning algorithm such as multiple linear regression on the total population and fertility rate data to do the exploratory data analysis about the future trend in the population and its impacts which would affect the wellfare of the nation. The correlation and prediction results showed an accuracy of 98%, with a declining trend of the fertility rate for the upcoming years as well. We evaluated the prediction model performance using root mean squared error and mean absolute error values in the training and testing of the model. Therefore, we concluded the paper with the future challenges for the country having such population trends.
AB - The Republic of Korea is experiencing a demographic crisis with low birth rates and aging population. As the present circumstance represents a developing danger to the supportability of its economy, schooling, accounts, and protection, there is a critical requirement for definite and comprehensive activity. South Korea is one among the world's quickest maturing nations. There might be a crucial impact forecasted if the country does not mitigate this growing jeopardy on the population. In this paper, we designed a prediction model using the machine learning algorithm such as multiple linear regression on the total population and fertility rate data to do the exploratory data analysis about the future trend in the population and its impacts which would affect the wellfare of the nation. The correlation and prediction results showed an accuracy of 98%, with a declining trend of the fertility rate for the upcoming years as well. We evaluated the prediction model performance using root mean squared error and mean absolute error values in the training and testing of the model. Therefore, we concluded the paper with the future challenges for the country having such population trends.
KW - Fertility rate
KW - Machine learning
KW - Multiple linear regression
KW - South Korea
KW - Total population
KW - Younger population
UR - http://www.scopus.com/inward/record.url?scp=85142713448&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-5037-7_55
DO - 10.1007/978-981-19-5037-7_55
M3 - Conference contribution
AN - SCOPUS:85142713448
SN - 9789811950360
T3 - Lecture Notes in Electrical Engineering
SP - 765
EP - 777
BT - Futuristic Trends in Networks and Computing Technologies - Select Proceedings of 4th International Conference on FTNCT 2021
A2 - Singh, Pradeep Kumar
A2 - Wierzchoń, Sławomir T.
A2 - Chhabra, Jitender Kumar
A2 - Tanwar, Sudeep
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021
Y2 - 10 December 2021 through 11 December 2021
ER -