SYSTEM ANALYSIS AND MACHINE LEARNING METHODS FOR OPTIMIZING CORPORATE RESOURCE MANAGEMENT

Kasymov Alexey A.

The purpose of this article is to study the methods of system analysis and machine learning to optimize the management of corporate resources in a small cafe. The research examines the methods of data collection, processing and analysis, as well as the training of machine learning models to improve management efficiency. The study used system analysis methods to identify resources and processes, as well as machine learning methods for data analysis and forecasting. To pre-process the data, they were cleaned, normalized and divided into training and test sets. The principal component method (PCA) was used to visualize and reduce the dimensionality of the data. As a result of the application of the developed methods, it was possible to automate routine tasks, improve the decision-making process and increase the overall efficiency of resource management in the cafe. The random forest model showed high accuracy of forecasts, which is confirmed by a low value of the standard error. The use of system analysis in combination with machine learning methods has proven its effectiveness in optimizing corporate resource management. In the future, it is recommended to continue the development of these methods, integrating additional data sources to achieve even better results.

Keywords:system analysis, machine learning, resource management, optimization, forecasting, automation