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Online Procurement Auctions up to 500 Thousand Rubles: Participants Behavior Patterns at Micro and Macrolevels

Student: Iuliia Ibragimova

Supervisor: Fedor Sandomirskiy

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Big Data Analysis for Business, Economy, and Society (Master)

Year of Graduation: 2018

Our work considers the problem of classification of fair and corrupted auctions up to 500 thousand rubles in the public procurement market in Russian Federation. This problem is complicated, because there are no labelled data available and sometimes the necessary information about the auction is not easy to retrieve. The work analyzes the data on auction bids and, based on the suspicious behavior patterns of participants found, the Bayesian classifier is designed to calculate the probability of corruption in an auction. It was shown that only with this information on the auction, it is possible to build a model that is simple enough for learning, but at the same time it gives results which are consistent with the results of similar studies and expert ratings. Moreover, the estimate of the level of corruption in the field of public procurement by regions of the Russian Federation was made.

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