Kazakhstan in the Global Economy: Rank, Growth, and Digital Adaptation from AI and Data Science Lens
DOI:
https://doi.org/10.52098/airdj.20255347Keywords:
global digital economy , Machine learning, principal component analysis, prediction modelsAbstract
This study focuses on Kazakhstan's place in the global digital economy and offers a machine learning-based framework for evaluating preparedness for digital adaptation. To categories countries into three maturity groups—Digital Leaders, Transition Economies, and Digital Late Adopters. This study used principal component analysis (PCA) and K-Means clustering on a synthesized dataset of digital transformation proxies, which included mobile penetration, broadband access, e-government development, and innovation scores. Kazakhstan was regularly classified as a Transition Economy, suggesting moderate advancement in policy innovation and digital infrastructure. The results of this study are encouraging, with a 69% accurate Decision Tree classifier with high precision in identifying Digital Leaders, which was developed to predict readiness class with GDP. However, its susceptibility to distinguishing middle- and lower-tier economies implies the sophistication of digital transformation beyond economic stature. These results offer a data-driven path for moving Kazakhstan closer to digital maturity through focused investments, structural reforms, and insightful information on Kazakhstan's strategic location.
References
Abusham, E. A., & Zaabi, A. M. A. (2021). Smartphone application for self-management diabetes: A review. Applied Computing Journal, 25–37.
Al-Shibli, A. S., & Abusham, E. (2017). Face recognition using local graph structure and support vector machine (LGS-SVM). International Journal of Computer Applications and Applied Sciences (IJOCAAS), 2(2), 68–72.
Abusham, E. E. A., & Bashier, H. K. (2013, August). Face recognition using local graph theory (LGT). In 2013 International Conference on Computing, Electrical and Electronic Engineering (ICCEEE) (pp. 593–596). IEEE.
Saini, D. K., Zia, K., & Abusham, E. (2019). Prediction market index by combining financial time-series forecasting and sentiment analysis using soft computing. In Distributed Computing and Artificial Intelligence, 15th International Conference (pp. 180–187). Springer International Publishing.
Kuziyeva, G. R., Rakhmatullaeva, D. O., Abdukadoriva, K. A., Babajanova, M. R., Botirova, X. O., & Kaxorova, A. N. (2022, December). Analysis of digital economy performance and economic growth nexus: Case of Uzbekistan. In Proceedings of the 6th International Conference on Future Networks & Distributed Systems (pp. 194-201).
Alisharipov, A., Kussainov, K., Yesmagambetov, D., & Arynova, Z. (2024). Elevating Citizen Engagement in the Process of Territorial Development through Digitisation. Digital Government: Research and Practice, 5(4), 1-12.
Sapakova, S., & Sapakov, A. (2024). Features of modeling the online real estate market in Almaty. Procedia Computer Science, 231, 409-414.
Okolo, C. T., Dell, N., & Vashistha, A. (2022, June). Making AI explainable in the Global South: A systematic review. In Proceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (pp. 439-452).
Mansurov, S., Abdurakhmanov, K., Islamov, B., & Zokirova, N. (2022, December). THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN LABOUR ECONOMICS: USING MIGRATION FROM CIS COUNTRIES TO RUSSIAN FEDERATION AS AN EXAMPLE. In Proceedings of the 6th International Conference on Future Networks & Distributed Systems (pp. 279-284).
Tolesh, F., & Biloshchytska, S. (2024). Forecasting international migration in Kazakhstan using ARIMA models. Procedia Computer Science, 231, 176-183.
Ramírez, K. M., Hormaza, J. M., & Soto, S. V. (2020, September). Artificial intelligence and its impact on the prediction of economic indicators. In Proceedings of the 6th International Conference on Engineering & MIS 2020 (pp. 1-8).
Hasanov, F. J., Mikayilov, J. I., Mukhtarov, S., & Suleymanov, E. (2019). Does CO2 emissions–economic growth relationship reveal EKC in developing countries? Evidence from Kazakhstan. Environmental Science and Pollution Research, 26(29), 30229-30241.
Shakenova, A. (2018). The role of small and medium enterprises in innovation activities in Kazakhstan= Kis és középvállalkozások szerepe az innovációs tevékenységekben Kazahsztánban. Köztes-Európa, 10(1), 79-86.
Alpysbaeva, S. N., Shuneev, S. Z., Bakdolotov, A. A., Beisengazin, K. S., Tautenov, E. I., & Zhanakova, N. N. (2022). Forecast of the Long-Term Trajectory of the Potential GDP of Kazakhstan in the Context of the Global Energy Transition. Studies on Russian Economic Development, 33(5), 561-570.
Feher, I., & Fieldsend, A. (2019). The potential for expanding wheat production and exports in Kazakhstan (No. JRC113009). Joint Research Centre.
Igilmanov, N. (2016). Problems of stable development of Kazakhstan. VUZF Review, (1), 40.
Kandiah, V., Escaith, H., & Shepelyansky, D. L. (2015). Google matrix of the world network of economic activities. The European Physical Journal B, 88, 1-20.
Tjia, L. Y. N. (2022). Kazakhstan’s leverage and economic diversification amid Chinese connectivity dreams. Third World Quarterly, 43(4), 797-822..
Uteubayev, T., Petrova, M., & Lyubenova, I. (2018, September). Training of qualified specialists in the process of their education in university: The role of the public-private partnership. In CBU International conference proceedings (Vol. 6, pp. 491-495).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Author(s) and ACAA permit unrestricted use, distribution, and reproduction in any medium, provided the original work with proper citation. This work is open access and licensed under Creative Commons Attribution International License (CC BY 4.0).
This work is licensed under a Creative Commons Attribution 4.0 International License.
ACAA applies the Creative Commons Attribution (CC BY 4.0) license to all published work. All ACAA content is open access that freely available for the public to unrestricted use, distribution, and reproduction in any medium, provided the original work with proper citation.
