Intelligent Algorithms for Evaluating Economic Indicators Based on Machine Learning Models
Keywords:
K-NN, decision tree, logistic regression, machine learning, economic indicators, classification problemAbstract
Ush tadqiqot sun'iy intellekt modellari asosida iqtisodiy ko'rinishlarni ishlab chiqish aqlli algoritmlarini ishlab chiqarishga chiqarish. Jumladan, tadqiqot iqtisodiy iqtisodiy ko'rsatkichlar sifatida qaraladigan talab va taklifning o'zaro bog'liqligini intellektual rivojlantirishga qaratilgan. Talab va taklif o'ziga xos nomutanosiblik sifatida qaraladigan mahsulot sotishdagi kechikishlarni boshorat qilish jarayoni har tomonlama tekshiriladi. Shu maqsadda muammo tasniflash tarzda shakllantiriladi va sun' intellektning mashina o'rganish modellari yordam beradi takliflar. iqtisodiy, iqtisodiy ko'rsatkichlarni amalga oshirish uchunistik regressiya, logistika qarorlari va K-Yaqin qo'shnilar (K-NN) usullariga barqaror aqlli algoritmlar ishlab chiqariladi. Taklif qilingan narsalarning yukini ko'rgan tajribaga ega bo'lgan.
References
Yang Z., Li D. “Application of Logistic Regression with Filter in Data Classification”, In Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China, 27–30 July 2019; pp. 3755–3759.
Ahmed Neloy A., Sadman Haque H.M., Ul Islam M. “Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring,” In Proceedings of the 2019 11th International Conference on Machine Learning and Computing, Zhuhai, China, 22-24 February 2019; pp. 350-356.
Friedman J.H. “Greedy Function Approximation: A Gradient Boosting Machine. Ann. Stat. 2001, 29, 1189-1232.
Gromping U. “Relative Importance for Linear Regression in R: The package relaimpo.” J. Stat. Softw. 2006,17, 27.
Ceh M., Kilibarda M., Lisec A., Bajat B. “Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments.” ISPRS Int. J. Geo-Inf. 2018, 7, 168.
Korobov M. “Explaining behavior of Machine Learning models with eli5 library.” In Proceedings of the EuroPython Congress 2017, Rimini, Italy, 9-16 July 2017.
Ho W.K.O., Tang B.-S., Wong, S.W. “Predicting property prices with machine learning algorithms.” J. Prop. Res. 2021, 38, 48-70.
Nazarov F.M., Yarmatov Sh.Sh. “Development of algorithms for predictive evaluation of investment projects based on machine learning,” Artificial Intelligence, Blockchain, Computing and Security Volume 2. eBook ISBN: 9781032684994, pages 681 – 685. 2024.
Chatterjee S., Simonoff J.S., “Handbook ofRegression Analysis; John Wiley & Sons Inc.: Hoboken, NJ, USA, 2013, p. 240.
FM Nazarov, S Yarmatov, M Xamidov. Machine learning price prediction on green building prices. 2024 International Russian Smart Industry Conference (SmartIndustryCon), 906-911.
F Bolikulov, R Nasimov, A Rashidov, F Akhmedov, C Young-Im. Effective methods of categorical data encoding for artificial intelligence algorithms. Mathematics 12 (16), 2553
Pai P.-F., Wang W.-C. “Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices. Appl. Sci. 2020, 10, 5832.
Sangani D., Erickson K., Hasan M. A., “Predicting Zillow Estimation Error Using Linear Regression and Gradient Boosting,” 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Orlando, FL, USA, 2017, pp. 530-534, doi: 10.1109/MASS.2017.88.
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