ON APPROACHES TO ANALYZING DEMOGRAPHIC DATA USING MACHINE LEARNING
Abstract
Demographic data are fairly accessible data sets that can be used for analysis with the use of modern technologies of artificial intelligence and machine learning (ML). However, they cannot be used for these purposes without special preparatory procedures. Preparatory measures include procedures involving work with signs, work with missing data, their normalization and design of signs. The article on the example of "Distribution of the population by age groups" shows the features of demographic data and suggests approaches for their preparation for the subsequent use of artificial intelligence technologies and machine learning for their analysis.
The study allowed us to obtain the following results. It has been established that demographic data has a number of features that can be and should be used in the process of improving the quality of data sets for subsequent work with them using artificial intelligence and machine learning technologies. The features of demographic data include, first of all, their temporal ordering, secondly, demographic data have predictable limits of change, which are determined by socio-economic factors, and the absence of significant differences between the closest values of the observed data.
Demographic data is influenced by processes in a sociopolitical and economic society in different historical periods, which must be taken into account when working with demographic data. Demographic data that can be attributed to certain historical periods should be given special attention since their values can both improve the quality of the data set for machine processing and cause the occurrence and growth of systematic and random errors. The proposed approaches can have a practical application to solving problems of population forecasting, determining the structure and composition of age groups, estimating life expectancy, determining the composition of the working (economically active) age population and a number of other tasks.
References
[2] Brink H., Richards J., Fetherolf M. Real-World Machine Learning. Manning, 2016. 264 p.
[3] Bughin J. et al. Artificial Intelligence: The Next Digital Frontier? Discussion Paper. McKinsey & Company, 2017. 78 p. Available at: https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx (accessed 23.09.2018).
[4] Lushnikov A.A., Kagan, A.I., Gvishiani, A.D., Lyubovtseva, Yu.S. Modeling of the evolutionary demographic processes for geomedicine. Geophysical Pprocesses and Biosphere. 2013; 12(3):5-18. Available at: https://elibrary.ru/item.asp?id=20265158 (accessed 23.09.2018). (In Russian)
[5] Poklonova E.V., Zakharenko P.V. Statistical analysis of the dynamics of the structure of the unemployed population in the Russian Federation. Problems of the Modern Economy (Novosibirsk). 2014; 22-2:164-171. Available at: https://elibrary.ru/item.asp?id=22676948 (accessed 23.09.2018). (In Russian)
[6] Bozhko T.N. Demographic assessment of the dynamics of the number and composition of the population for 2010-2016. Proceedings of the Actual problems of the development of economic entities, territories and systems of regional and municipal government. 2017, pp. 9-13. Available at: https://elibrary.ru/item.asp?id=29371066 (accessed 23.09.2018). (In Russian)
[7] Karamnova L.V., Koporova M.A. Prospects for creating artificial intelligence. Gagarin Readings 2017. М.: МАI, 2017, pp. 1178-1179. Available at: https://elibrary.ru/item.asp?id=30084898 (accessed 23.09.2018). (In Russian)
[8] Daragan A.D., Ezhov G.L., Ezhov G.A. On the methodological aspects of the implementation of training procedures for artificial intelligence systems. Modern Pedagogical Education. 2017; 3:23-24. Available at: https://elibrary.ru/item.asp?id=30731184 (accessed 23.09.2018). (In Russian)
[9] Andreev E.M., Vishnevsky A.G. The nearest demographic perspectives of Russia. Demoscope Weekly. 2014; 601-602:1-25. Available at: http://www.demoscope.ru/weekly/2014/0601/demoscope601.pdf (accessed 23.09.2018). (In Russian)
[10] Moskvitin A.A., Soziev T.M. Features of modern methods of data mining. Modern Methods of Data Mining in Economic, Humanitarian, and Natural Sciences. Proceedings of the international scientific-practical conference. Pyatigorsk, 2016. pp. 11-18. Available at: https://elibrary.ru/item.asp?id=30088652 (accessed 23.09.2018). (In Russian)
[11] Krivonosov N.A. The use of artificial intelligence as a means of analyzing / evaluating the activities of logistics companies. A Step to the Future: Artificial Intelligence and the Digital Economy. Proceedings of the 1st International Scientific Practical Conference. Vol. 3. M.: SUM, 2017, pp. 99-106. Available at: https://elibrary.ru/item.asp?id=32772302 (accessed 23.09.2018). (In Russian)
[12] Pidyashova O.P. Studies of the standard of living of the population in modern conditions (regional aspect). Journal of Economy and entrepreneurship. 2015; 12-1:1083-1090. Available at: https://elibrary.ru/item.asp?id=25031618 (accessed 23.09.2018). (In Russian)
[13] Solovev A.I. A Person in a Digital Economy: Analog or Discrete? Scientific Works of the Free Economic Society of Russia. 2018; 210(2):130-134. Available at: https://elibrary.ru/item.asp?id=35041906 (accessed 23.09.2018). (In Russian)
[14] Kupriyanovskiy V.P., Sotnikov A.E., Solovev A.I., Drozhzhinov V.I., Namiot D.E., Mamaev V.Yu., Kupriyanovskiy P.V. AADHAAR - Identification of the Person in the Digital Economy. International Journal of Open Information Technologies. 2017; 5(2):34-45. Available at: https://elibrary.ru/item.asp?id=28314924 (accessed 23.09.2018). (In Russian)
[15] Gepp A., Linnenluecke M.K., O’Neill T.J. Tom Smith Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature. 2018; 40:102-115. DIO: 10.1016/j.acclit.2017.05.003
[16] Kauffman R.J., Kim K., Lee S-Y.T. Hoang A-P., Ren J. Combining machine-based and econometrics methods for policy analytics insights. Electronic Commerce Research and Applications. 2017; 25(C):115-140. DOI: 10.1016/j.elerap.2017.04.004
[17] Dremel С., Herterich M.M., Wulf J., vom Brocke J. Actualizing Big Data Analytics Affordances: A Revelatory Case Study. Information & Management. 2018. DOI: 10.1016/j.im.2018.10.007
[18] McClean S.I. Data Mining and Knowledge Discovery. Encyclopedia of Physical Science and Technology. (Third Edition), 2003, pp. 229-246. DOI: 10.1016/B0-12-227410-5/00845-0
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