Determining fulfilment rate of public procurement contracts

This paper investigates the problem of predicting the success of contracts concluded in Russia. It is based on a machine learning algorithm: a gradient binning over solver trees. The classifier parameters are adjusted, and the most important features are generated and searched for. The following important attributes were found: percentage of contract price drop; contract price per day; contract price per employee; contract price multiplied by price change.