Keywords: mathematical modeling, software package, data analysis, government contracts, machine learning, intelligent system, forecasting
Mathematical models and software complex for intelligent analysis and forecasting the performance of government contracts
UDC 004.048
DOI: 10.26102/2310-6018/2025.48.1.010
The paper proposes mathematical models and a software package for intellectual analysis and forecasting of the execution of government contracts, based on a neural network and classical machine learning methods trained on a retrospective database of counterparties and contracts. A set of mathematical models and programs allows you to calculate the probabilities and risks of non-fulfillment of government contracts, thereby reducing budget losses and positively influencing the stability of the real sector of the economy. A comparative analysis of machine learning methods was carried out: logistic regression, decision tree, support vector machine and neural network model. A model has been developed that allows forecasting with an accuracy of 97.89%. For each mathematical model, a separate module has been developed, which together constitute a software package. The neural network model showed a result of 87.65%, which is associated with a relatively small set of data for training; however, this model allows us to reveal the further potential of the system in connection with continuous training in real time on new contracts, for the evaluation of which the proposed software package will be used. The results of the study can be used to further improve decision support systems in the field of procurement and its application in order to improve the overall quality of analysis and forecasting of the implementation of government contracts.
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Keywords: mathematical modeling, software package, data analysis, government contracts, machine learning, intelligent system, forecasting
For citation: Rubtsov D.Y. Mathematical models and software complex for intelligent analysis and forecasting the performance of government contracts. Modeling, Optimization and Information Technology. 2025;13(1). URL: https://moitvivt.ru/ru/journal/pdf?id=1778 DOI: 10.26102/2310-6018/2025.48.1.010 (In Russ).
Received 20.12.2024
Revised 20.01.2025
Accepted 23.01.2025