Geodesic
Determining fulfilment rate of public procurement contracts
Fundamentalʹnaâ i prikladnaâ matematika, Tome 25 (2024) no. 1, pp. 237-250 Cet article a éte moissonné depuis la source Math-Net.Ru

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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. 
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M. A. Khromov; A. V. Shokurov. Determining fulfilment rate of public procurement contracts. Fundamentalʹnaâ i prikladnaâ matematika, Tome 25 (2024) no. 1, pp. 237-250. http://geodesic.mathdoc.fr/item/FPM_2024_25_1_a13/

[1] Administrativno-upravlencheskii portal, http://www.aup.ru/okpd/

[2] Ponyatie i forma nezavisimoi garantii, Grazhdanskii kodeks RF. Statya 368

[3] Dyakonov I. D., Novikova S. V., “Reshenie zadachi prognozirovaniya pri pomoschi gradientnogo bustinga nad reshayuschimi derevyami”, XV Mezhdunarodnaya nauchno-prakticheskaya konferentsiya «Nauchnyi forum: tekhnicheskie i fiziko-matematicheskie nauki» https://nauchforum.ru/conf/tech/xv/35400

[4] Otkrytyi kurs mashinnogo obucheniya. Tema 10. Gradientnyi busting, https://habr.com/ru/company/ods/blog/327250/

[5] Sberbank-AST — elektronnaya torgovaya ploschadka, https://www.sberbank-ast.ru

[6] Federalnyi zakon ot 05.04.2013 No 44-FZ (red. ot 01.05.2019) «O kontraktnoi sisteme v sfere zakupok tovarov, rabot, uslug dlya obespecheniya gosudarstvennykh i munitsipalnykh nuzhd»

[7] Khanzhin S. V., “Matematicheskaya model kreditnogo skoringa potentsialnykh klientov banka”, Fundament. i priklad. issled.: problemy i rezultaty, sb. mater. XX Mezhdunar. nauch.-prakt. konf. (Novosibirsk, 2015), Izd-vo TsRNS, Novosibirsk, 2015, 184–188 https://cyberleninka.ru/article/n/matematicheskaya-model-kreditnogo-skoringa-potentsialnyh-klientov-banka.pdf

[8] Freund Y., Schapire R., “Experiments with a new boosting algorithm”, Machine Learning, Proc. of the Thirteenth Int. Conf., 1996 | MR

[9] Friedman J., “Greedy function approximation: A gradient boosting machine”, IMS 1999, Reitz Lecture https://jerryfriedman.su.domains/ftp/trebst.pdf

[10] Mitchell T., Machine Learning, McGraw-Hill, 1997 | Zbl

[11] One-hot, https://academy.yandex.ru/handbook/ml/article/linear-models

[12] Seldon. Basis, https://basis.myseldon.com/ru/landing