Café’s Performance Modeling with Spatial Data
( Pp. 167-178)
More about authors
Ivan D. Ivanov
Head, postgraduate student
LLC «BST Digital»; The Russian Presidential Academy of National Economy and Public Administration
Moscow, Russian Federation; Moscow, Russian Federation Nailia H. Abliazina the EMIT Institute
The Russian Presidential Academy of National Economy and Public Administration
Moscow, Russian Federation Natalia V. Grineva Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Information Technology
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
LLC «BST Digital»; The Russian Presidential Academy of National Economy and Public Administration
Moscow, Russian Federation; Moscow, Russian Federation Nailia H. Abliazina the EMIT Institute
The Russian Presidential Academy of National Economy and Public Administration
Moscow, Russian Federation Natalia V. Grineva Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Information Technology
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Abstract:
The relevance of the article lies in the importance of the placement problem for the economic performance of organizations and the growth of interest in the use of spatial data in decision support systems in recent years. The main purpose of the research work is to model the estimation of impact of important spatial features for café’s turnover prediction. The article analyzes some approaches that combine spatial data with machine learning to solve the placement problem. A correlation analysis of spatial data has been carried out. A multistage feature selection for two sets of features proper for different types of models was made. The hyperparameter optimization for the selected modeling methods (linear regression, decision tree, random forest, gradient boosting) was made and models were created. The main tools are the Python programming language and its libraries pandas, sklearn, XGBoost, hyperopt, shap, boostaroota. The analysis of the obtained results was carried out. The gradient boosting model was identified as optimal in terms of accuracy and interpretation. The result of the work is the created approach to modeling the economic performance of a company using machine learning based on spatial data.
How to Cite:
Ivanov I. D., Abliazina N. H., Grineva N. V. Café’s Performance Modeling with Spatial Data // ECONOMIC PROBLEMS AND LEGAL PRACTICE. 2023. Vol. 19. № 3. P. 167-178. (in Russ.) EDN: MFRRXN
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Keywords:
Python., spatial data, economic indicators, machine learning, Python..
