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The paper considers the problem of developing artificial intelligence tools in the field of project management. Models for predicting missed deadlines within the framework of national project checkpoints are prepared using machine learning methods. For this purpose, anonymized and normalized data of the monitoring system for 2022-2024 with a total volume of 4,783 records are used. The task is to perform binary classification. The data for 2022-2023 were used to train models (20% of data withheld as test data) while 2024 year data were used to validate the resulting models. Cross-validation and oversampling methods are used to eliminate class imbalance. The results indicate a difference in data between the years. Because of this, the use of models trained on the first two years data for predicting based on the third year data shows slightly lower scores. Numerical quality scores of the models are presented and discussed. Possible directions for future research are noted.

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Machine Learning Methods for Deadline Missing Prediction Using National Project Checkpoint Data

  • 会议论文

  • 发表日期：

    2026年

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  • Alexander Albychev,
  • Alexander Chervyakov,
  • Nurziya Gazanova,
  • Dmitry Ilin,
  • Evgeny Nikulchev

摘要

The paper considers the problem of developing artificial intelligence tools in the field of project management. Models for predicting missed deadlines within the framework of national project checkpoints are prepared using machine learning methods. For this purpose, anonymized and normalized data of the monitoring system for 2022-2024 with a total volume of 4,783 records are used. The task is to perform binary classification. The data for 2022-2023 were used to train models (20% of data withheld as test data) while 2024 year data were used to validate the resulting models. Cross-validation and oversampling methods are used to eliminate class imbalance. The results indicate a difference in data between the years. Because of this, the use of models trained on the first two years data for predicting based on the third year data shows slightly lower scores. Numerical quality scores of the models are presented and discussed. Possible directions for future research are noted.