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Application of machine learning methods in big data analytics at management of contracts in the construction industry

机译：机器学习方法在建筑业合同管理中的大数据分析中的应用

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摘要

The number of experts who realize the importance of big data continues to increase in various fields of the economy. Experts begin to use big data more frequently for the solution of their specific objectives. One of the probable big data tasks in the construction industry is the determination of the probability of contract execution at a stage of its establishment. The contract holder cannot guarantee execution of the contract. Therefore it leads to a lot of risks for the customer. This article is devoted to the applicability of machine learning methods to the task of determination of the probability of a successful contract execution. Authors try to reveal the factors influencing the possibility of contract default and then try to define the following corrective actions for a customer. In the problem analysis, authors used the linear and non-linear algorithms, feature extraction, feature transformation and feature selection. The results of investigation include the prognostic models with a predictive force based on the machine learning algorithms such as logistic regression, decision tree, randomize forest. Authors have validated models on available historical data. The developed models have the potential for practical use in the construction organizations while making new contracts.
机译：在经济的各个领域中，意识到大数据的重要性的专家人数在不断增加。专家开始更频繁地使用大数据来解决其特定目标。建筑行业可能的大数据任务之一是在合同建立阶段确定合同的执行概率。合同持有人不能保证合同的执行。因此，这给客户带来很多风险。本文致力于将机器学习方法应用于确定成功执行合同的可能性的任务。作者试图揭示影响合同违约可能性的因素，然后尝试为客户定义以下纠正措施。在问题分析中，作者使用了线性和非线性算法，特征提取，特征变换和特征选择。调查结果包括基于机器学习算法（如逻辑回归，决策树，随机森林）的具有预测力的预后模型。作者已经根据可用的历史数据验证了模型。所开发的模型在签订新合同时具有在建筑组织中实际使用的潜力。

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