title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾ÑÐ±Ð¾ÑÐ° Ð¸ Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²ÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð² Ð±Ð¸Ð·Ð½ÐµÑ-Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine learning algorithms in usage to select and prepare data in business analysis
Creators: name: ÐÐ¸ÑÑÑÐ¸Ð½, ÐÐ¸ÐºÐ¾Ð»Ð°Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
machine learning
data analysis
neural networks
correlation analysis
prediction

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description: The given work is devoted to form a clear, universal approach to data selection and the formation of a training sample for solving problems using machine learning algorithms, as well as to study and put into practice a new coefficient of agreement in dynamics, which is used in correlation analysis. The research set the following goals: â form a problem field, describe the influence of factors arising from the use of unprepared or unverified data in solving problems using AI; â substantiate the importance of the problem, describe the positive effects of applying a universal approach to data preparation; â assess the degree of knowledge of the problem by analyzing scientific papers on the subject; â conduct a study of the approach to data selection and formation of a training sample, as well as to study a new mechanism for estimating relationships between time series; â to simulate the solution of the problem of forecasting commodity demand using machine learning algorithms based on the methods obtained as a result of the study; â form conclusions about the further development of the studied approach. The work was fulfilled on the premises of popularity of applications of machine learning algorithms and insufficient coverage of the problem of the quality of input data for learning the decision algorithms. The sources of information were data from domestic and foreign research literature and official Internet resources. An approach to the formation of a high-quality training sample for machine learning is proposed, as well as the practical application of a new tool for assessing correlation relationships.