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Method of Forming a Training Sample for Segmentation of Tender Organizers on Machine Learning Basis

Paper develops the method of a training sample forming for training the segmentation of tender organizers on the basis of machine learning. To segment the tender’s organizers on the machine learning basis, it is necessary to form an ideal training sample. This will allow segmenting tender organizers into the following groups: The best tenders organizers, Loyal tenders organizers, Large consumers, Seldom tenders organizer, but for a large sum, and Weak tender organizers. The method is based on RTF analysis and K-means clustering. Completed agreements of tender participants in Ukraine from the ProZorro Sales site were used as input data. The sample is 93,336 values relative to 10 parameters. The result was tested using Logistic Regression and Naive Bayes, which demonstrated 100% accuracy.