{"id":378644,"date":"2024-06-11T21:00:18","date_gmt":"2024-06-11T21:00:18","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=378644"},"modified":"-0001-11-30T00:00:00","modified_gmt":"2024-06-11T21:00:18","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=378644","title":{"rendered":"\u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430 \u0438 \u043f\u043e\u0431\u0435\u0434\u0438\u0442\u0435\u043b\u044f \u0415\u0432\u0440\u043e 2024 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u0447\u0430\u0442\u0430 GPT 4.0<\/span>"},"content":{"rendered":"
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\u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440<\/strong><\/p>\n

\u041f\u043e \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u043d\u0438\u0436\u0435 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0442\u0430\u0432\u043a\u0438, \u0442.\u043a. \u043e\u043d\u0438 \u043d\u0435 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044e\u0442 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u0443\u044e \u043c\u0430\u0440\u0436\u0443, \u0444\u043e\u0440\u043c\u0443 \u043a\u043e\u043c\u0430\u043d\u0434, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043c\u043d\u043e\u0433\u043e \u0434\u0440\u0443\u0433\u0438\u0445 \u0444\u0430\u043a\u0442\u043e\u0440\u043e\u0432. \u0412 \u0446\u0435\u043b\u043e\u043c \u0441\u0442\u0430\u0432\u043a\u0438 \u043d\u0430 \u0441\u043f\u043e\u0440\u0442 \u0432\u0435\u0441\u044c\u043c\u0430 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u0447\u043d\u043e\u0435 \u0437\u0430\u043d\u044f\u0442\u0438\u0435, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u043e\u0435 \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u043d\u0430 \u043f\u0441\u0438\u0445\u043e\u043b\u043e\u0433\u0438\u044e \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430, \u0441\u043a\u0440\u044b\u0442\u044b\u0435 \u0441\u043b\u0430\u0431\u043e\u0441\u0442\u0438 \u0438 \u0442. \u0434., \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0432 \u0446\u0435\u043b\u043e\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043b\u044e\u0431\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435. <\/p>\n<\/p>\n

\u0414\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u044f \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0438\u0441\u044c: \u0434\u0430\u0442\u0430\u0441\u0435\u0442: https:\/\/www.kaggle.com\/datasets\/martj42\/international-football-results-from-1872-to-2017?resource=download<\/a> , \u044f\u0437\u044b\u043a \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f R, \u0447\u0430\u0442 GPT 4.0.\u00a0<\/p>\n

\u0426\u0435\u043b\u0438 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: 1) \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u0431\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445 \u0437\u0430 20 \u043b\u0435\u0442 2) \u0443\u0437\u043d\u0430\u0442\u044c \u0440\u0430\u0437\u043c\u0435\u0440 \u0432\u044b\u0438\u0433\u0440\u044b\u0448\u0430\/\u043f\u0440\u043e\u0438\u0433\u0440\u044b\u0448\u0430 \u0432 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u043e\u0439 \u043a\u043e\u043d\u0442\u043e\u0440\u0435 \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430. <\/p>\n

\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0430\u0442\u0438\u043a\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: \u0437\u0434\u0435\u0441\u044c \u043d\u0435 \u0443\u0447\u0442\u0435\u043d\u044b \u0432\u0430\u0436\u043d\u044b\u0435 \u0444\u0430\u043a\u0442\u043e\u0440\u044b \u0442\u0430\u043a\u0438\u0435 \u043a\u0430\u043a \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0438 \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u0438\u0433\u0440\u043e\u043a\u043e\u0432, \u0442\u0435\u043a\u0443\u0449\u0430\u044f \u0444\u043e\u0440\u043c\u0430 \u043a\u043e\u043c\u0430\u043d\u0434, \u0444\u0430\u043a\u0442\u043e\u0440 \u0434\u043e\u043c\u0430\u0448\u043d\u0435\u0433\u043e \u0442\u0443\u0440\u043d\u0438\u0440\u0430 \u0434\u043b\u044f \u0441\u0431\u043e\u0440\u043d\u043e\u0439 \u0413\u0435\u0440\u043c\u0430\u043d\u0438\u0438 \u0438 \u043c\u043d\u043e\u0433\u043e\u0435 \u0434\u0440\u0443\u0433\u043e\u0435.<\/p>\n

\u042d\u0442\u043e \u0432\u0441\u0435 \u0443\u0447\u0442\u0435\u043d\u043e \u0441\u0430\u043c\u0438\u043c\u0438 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430\u043c\u0438 \u043f\u0440\u0438 \u0432\u044b\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432 \u043d\u0430 \u043c\u0430\u0442\u0447\u0438, \u043c\u0438\u043d\u0443\u0441 10-15 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u0438\u0445 \u043c\u0430\u0440\u0436\u0438, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u044b\u0431\u0438\u0440\u0430\u044f \u0444\u0430\u0432\u043e\u0440\u0438\u0442\u043e\u0432 \u0432\u044b\u0438\u0433\u0440\u0430\u0442\u044c \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e. <\/p>\n

\u041b\u0438\u0447\u043d\u043e \u0434\u043b\u044f \u043c\u0435\u043d\u044f \u0431\u043e\u043b\u044c\u0448\u0438\u0439 \u0438\u043d\u0442\u0435\u0440\u0435\u0441 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u043e\u0442\u0432\u0435\u0442 \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441 — \u0443\u0434\u0430\u0441\u0442\u0441\u044f \u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u0435 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0442\u044c \u043d\u0435\u043e\u0447\u0435\u0432\u0438\u0434\u043d\u044b\u0435 \u0437\u0430\u043a\u043e\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u0438 \u0438 \u043e\u0431\u044b\u0433\u0440\u0430\u0442\u044c \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430, \u0430 \u043d\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0444\u0430\u0432\u043e\u0440\u0438\u0442\u0430. <\/p>\n

\u00a0<\/p>\n

\u041c\u0435\u0442\u043e\u0434\u043e\u043b\u043e\u0433\u0438\u044f <\/p>\n

\u0412 \u043f\u0435\u0440\u0432\u0443\u044e \u043e\u0447\u0435\u0440\u0435\u0434\u044c \u0431\u044b\u043b \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u0430\u0442\u0430\u0441\u0435\u0442, \u0442\u0430\u043a \u043a\u0430\u043a \u043e\u043d \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0431\u043e\u043b\u0435\u0435 47 000 \u043c\u0430\u0442\u0447\u0435\u0439 \u0437\u0430 152 \u0433\u043e\u0434\u0430, \u0432 \u0442\u043e\u043c \u0447\u0438\u0441\u043b\u0435 — \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u0444\u0440\u0438\u043a\u0430\u043d\u0441\u043a\u0438\u0445 \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u043c \u043d\u0435 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u044b \u0438 \u0437\u0430\u043c\u0435\u0434\u043b\u0438\u043b\u0438 \u0431\u044b \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0431\u044b\u043b \u0441\u043e\u043a\u0440\u0430\u0449\u0435\u043d \u0434\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0435\u0432\u0440\u043e, \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u043a \u043d\u0435\u043c\u0443 \u0438 \u043b\u0438\u0433\u0435 \u043d\u0430\u0446\u0438\u0439. <\/p>\n

*\u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044e \u043a \u043c\u0438\u0440\u0443 \u0431\u0440\u0430\u0442\u044c \u043d\u0435 \u0441\u0442\u0430\u043b, \u0442.\u043a. \u0445\u043e\u0442\u044f \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0442\u0435 \u0436\u0435, \u044d\u0442\u043e \u0434\u0440\u0443\u0433\u043e\u0439 \u0442\u0443\u0440\u043d\u0438\u0440 \u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u043e\u0442\u043b\u0438\u0447\u0430\u0435\u0442\u0441\u044f*<\/em><\/p>\n

\u0417\u0430 \u0442\u043e\u0447\u043a\u0443 \u043e\u0442\u0441\u0447\u0451\u0442\u0430 \u0432\u0437\u044f\u0442 \u0435\u0432\u0440\u043e 1996 \u0433\u043e\u0434\u0430 \u0438 \u043a\u0430\u043a \u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u0435 \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u043a \u043d\u0435\u043c\u0443, \u043d\u0430\u0447\u0438\u043d\u0430\u044f \u0441 1994 \u0433\u043e\u0434\u0430. \u0422\u0430\u043a\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0441\u0432\u044f\u0437\u0430\u043d\u043e \u0441 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0430 \u0442\u0443\u0440\u043d\u0438\u0440\u0430, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0441 \u0440\u0430\u0437\u0432\u0430\u043b\u043e\u043c \u0441\u0442\u0440\u0430\u043d \u0441\u043e\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0431\u043b\u043e\u043a\u0430 (\u0443\u0432\u0435\u043b\u0438\u0447\u0438\u043b\u043e\u0441\u044c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u0442\u0440\u0430\u043d \u0443\u0447\u0430\u0441\u0442\u043d\u0438\u0446). <\/p>\n

\u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043c\u044b \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u043e\u0434\u0438\u043d \u0441\u043e\u0441\u0442\u0430\u0432 \u0443\u0447\u0430\u0441\u0442\u043d\u0438\u043a\u043e\u0432 \u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0437\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 20 \u043b\u0435\u0442. \u0424\u0438\u043d\u0430\u043b\u044c\u043d\u044b\u0439 \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u043b 2 758 \u043c\u0430\u0442\u0447\u0435\u0439. <\/p>\n

\u00a0<\/p>\n

\u0414\u0430\u043b\u0435\u0435 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0447\u0430\u0442\u0430 GPT \u044f \u043f\u0435\u0440\u0435\u0431\u0440\u0430\u043b \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0432 Python (\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b: pandas, numpy, train_test_split, GridSearchCV, RandomForestClassifier, accuracy_score). <\/p>\n

\u041b\u0443\u0447\u0448\u0438\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u0441\u0442\u0430\u043b\u0430 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 — 53.51%.<\/p>\n

\u0422\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u043e\u0441\u044c \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u044f\u0437\u044b\u043a R.<\/p>\n

\u041b\u0443\u0447\u0448\u0438\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u043d\u0430 R \u0441\u0442\u0430\u043b\u0430 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 — 57.65%<\/p>\n

\u0412\u0435\u0441\u044c\u043c\u0430 \u043d\u0435\u043f\u043b\u043e\u0445\u043e\u0439 \u043f\u0440\u043e\u0446\u0435\u043d\u0442, \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044f, \u0447\u0442\u043e \u0438\u0433\u0440\u0430 \u0438\u0434\u0435\u0442 \u043d\u0430 3 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430. \u0422\u0430\u043a \u043a\u0430\u043a \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u043d\u0430 R \u0443 \u043d\u0430\u0441 \u0432\u044b\u0448\u0435, \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0435\u0433\u043e \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.<\/p>\n

> library(randomForest) > library(dplyr) >  > # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 > data <- read.csv(\"filtered_results.csv\") >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b > data$date <- as.Date(data$date, format=\"%Y-%m-%d\") >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 > data$result <- ifelse(data$home_score > data$away_score, 1,  +                       ifelse(data$home_score < data$away_score, -1, 0)) >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 > home_games <- data %>% +     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result) >  > away_games <- data %>% +     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>% +     mutate(result = ifelse(result == 1, -1, ifelse(result == -1, 1, 0))) >  > all_games <- bind_rows(home_games, away_games) >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 > team_stats <- all_games %>% +     group_by(team) %>% +     summarise(total_games = n(), +               total_win_rate = mean(result == 1), +               total_avg_score = mean(score)) >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > data <- data %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_games = total_games, +            home_team_total_win_rate = total_win_rate, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_games = total_games, +            away_team_total_win_rate = total_win_rate, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > data[is.na(data)] <- 0 >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > features <- c(\"home_team_total_win_rate\", \"away_team_total_win_rate\",  +               \"home_team_total_games\", \"away_team_total_games\",  +               \"home_team_total_avg_score\", \"away_team_total_avg_score\") > X <- data[features] > y <- factor(data$result) >  > # \u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438 > set.seed(42) > train_indices <- sample(seq_len(nrow(data)), size = 0.8 * nrow(data)) > X_train <- X[train_indices, ] > y_train <- y[train_indices] > X_test <- X[-train_indices, ] > y_test <- y[-train_indices] >  > # \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 Random Forest > rf_model <- randomForest(X_train, y_train, ntree=200, mtry=3, importance=TRUE) >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435 > y_pred <- predict(rf_model, X_test) > accuracy <- sum(y_pred == y_test) \/ length(y_test) > print(paste(\"Accuracy:\", accuracy)) [1] \"Accuracy: 0.576576576576577\" >  > # \u041f\u0440\u0438\u043c\u0435\u0440 \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > new_matches <- data.frame( +     home_team = c(\"Germany\", \"Hungary\", \"Spain\", \"Italy\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Austria\",  +                   \"Turkey\", \"Portugal\", \"Croatia\", \"Germany\", \"Scotland\", \"Slovenia\", \"Denmark\", \"Spain\", \"Slovakia\",  +                   \"Poland\", \"Netherlands\", \"Georgia\", \"Turkey\", \"Belgium\", \"Switzerland\", \"Scotland\", \"Albania\", \"Croatia\",  +                   \"Netherlands\", \"France\", \"England\", \"Denmark\", \"Slovakia\", \"Ukraine\", \"Georgia\", \"Czech Republic\"), +     away_team = c(\"Scotland\", \"Switzerland\", \"Croatia\", \"Albania\", \"Netherlands\", \"Denmark\", \"England\", \"Ukraine\", \"Slovakia\",  +                   \"France\", \"Georgia\", \"Czech Republic\", \"Albania\", \"Hungary\", \"Switzerland\", \"Serbia\", \"England\", \"Italy\",  +                   \"Ukraine\", \"Austria\", \"France\", \"Czech Republic\", \"Portugal\", \"Romania\", \"Germany\", \"Hungary\", \"Spain\",  +                   \"Italy\", \"Austria\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Portugal\", \"Turkey\") + ) >  > # \u0420\u0430\u0441\u0447\u0435\u0442 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0434\u043b\u044f \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > new_matches <- new_matches %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_win_rate = total_win_rate, +            home_team_total_games = total_games, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_win_rate = total_win_rate, +            away_team_total_games = total_games, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > new_matches[is.na(new_matches)] <- 0 >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > predictions <- predict(rf_model, new_matches[features]) > results <- ifelse(predictions == 1, \"Home Win\", ifelse(predictions == 0, \"Draw\", \"Away Win\")) >  > # \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 > for (i in 1:nrow(new_matches)) { +     print(paste(new_matches$home_team[i], \"vs\", new_matches$away_team[i], \"-> Prediction:\", results[i]))<\/code><\/pre>\n
\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430:<\/p>\n
1. Germany vs Scotland -> Prediction: Home Win<\/p>\n
2. Hungary vs Switzerland -> Prediction: Home Win<\/p>\n
3. Spain vs Croatia -> Prediction: Home Win<\/p>\n
4. Italy vs Albania -> Prediction: Home Win<\/p>\n
5. Poland vs Netherlands -> Prediction: Away Win<\/p>\n
6. Slovenia vs Denmark -> Prediction: Draw<\/p>\n
7. Serbia vs England -> Prediction: Draw<\/p>\n
8. Romania vs Ukraine -> Prediction: Home Win<\/p>\n
9. Belgium vs Slovakia -> Prediction: Home Win<\/p>\n
10. Austria vs France -> Prediction: Away Win<\/p>\n
11. Turkey vs Georgia -> Prediction: Home Win<\/p>\n
12. Portugal vs Czech Republic -> Prediction: Home Win<\/p>\n
13. Croatia vs Albania -> Prediction: Home Win<\/p>\n
14. Germany vs Hungary -> Prediction: Home Win<\/p>\n
15. Scotland vs Switzerland -> Prediction: Home Win<\/p>\n
16. Slovenia vs Serbia -> Prediction: Home Win<\/p>\n
17. Denmark vs England -> Prediction: Draw<\/p>\n
18. Spain vs Italy -> Prediction: Home Win<\/p>\n
19. Slovakia vs Ukraine -> Prediction: Home Win<\/p>\n
20. Poland vs Austria -> Prediction: Home Win<\/p>\n
21. Netherlands vs France -> Prediction: Away Win<\/p>\n
22. Georgia vs Czech Republic -> Prediction: Away Win<\/p>\n
23. Turkey vs Portugal -> Prediction: Away Win<\/p>\n
24. Belgium vs Romania -> Prediction: Draw<\/p>\n
25. Switzerland vs Germany -> Prediction: Away Win<\/p>\n
26. Scotland vs Hungary -> Prediction: Home Win<\/p>\n
27. Albania vs Spain -> Prediction: Away Win<\/p>\n
28. Croatia vs Italy -> Prediction: Draw<\/p>\n
29. Netherlands vs Austria -> Prediction: Home Win<\/p>\n
30. France vs Poland -> Prediction: Home Win<\/p>\n
31. England vs Slovenia -> Prediction: Home Win<\/p>\n
32. Denmark vs Serbia -> Prediction: Home Win<\/p>\n
33. Slovakia vs Romania -> Prediction: Away Win<\/p>\n
34. Ukraine vs Belgium -> Prediction: Home Win<\/p>\n
35. Georgia vs Portugal -> Prediction: Away Win<\/p>\n
36. Czech Republic vs Turkey -> Prediction: Home Win<\/p>\n
\u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0441\u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043b\u0430\u0441\u044c \u0441\u0435\u0442\u043a\u0430 1\/8 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043c\u0430\u0442\u0447\u0435\u0439. <\/p>\n
library(randomForest) > library(dplyr) >  > # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 > data <- read.csv(\"filtered_results.csv\") >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b > data$date <- as.Date(data$date, format=\"%Y-%m-%d\") >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 > data$result <- ifelse(data$home_score > data$away_score, 1,  +                       ifelse(data$home_score < data$away_score, -1, 0)) >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 > home_games <- data %>% +     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result) >  > away_games <- data %>% +     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>% +     mutate(result = ifelse(result == 1, -1, ifelse(result == -1, 1, 0))) >  > all_games <- bind_rows(home_games, away_games) >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 > team_stats <- all_games %>% +     group_by(team) %>% +     summarise(total_games = n(), +               total_win_rate = mean(result == 1), +               total_avg_score = mean(score)) >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > data <- data %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_games = total_games, +            home_team_total_win_rate = total_win_rate, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_games = total_games, +            away_team_total_win_rate = total_win_rate, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > data[is.na(data)] <- 0 >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > features <- c(\"home_team_total_win_rate\", \"away_team_total_win_rate\",  +               \"home_team_total_games\", \"away_team_total_games\",  +               \"home_team_total_avg_score\", \"away_team_total_avg_score\") > X <- data[features] > y <- factor(data$result) >  > # \u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438 > set.seed(42) > train_indices <- sample(seq_len(nrow(data)), size = 0.8 * nrow(data)) > X_train <- X[train_indices, ] > y_train <- y[train_indices] > X_test <- X[-train_indices, ] > y_test <- y[-train_indices] >  > # \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 Random Forest > rf_model <- randomForest(X_train, y_train, ntree=200, mtry=3, importance=TRUE) >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435 > y_pred <- predict(rf_model, X_test) > accuracy <- sum(y_pred == y_test) \/ length(y_test) > print(paste(\"Accuracy:\", accuracy)) [1] \"Accuracy: 0.576576576576577\" >  > # \u0413\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0439 \u044d\u0442\u0430\u043f > group_stage_matches <- data.frame( +     home_team = c(\"Germany\", \"Hungary\", \"Spain\", \"Italy\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Austria\",  +                   \"Turkey\", \"Portugal\", \"Croatia\", \"Germany\", \"Scotland\", \"Slovenia\", \"Denmark\", \"Spain\", \"Slovakia\",  +                   \"Poland\", \"Netherlands\", \"Georgia\", \"Turkey\", \"Belgium\", \"Switzerland\", \"Scotland\", \"Albania\", \"Croatia\",  +                   \"Netherlands\", \"France\", \"England\", \"Denmark\", \"Slovakia\", \"Ukraine\", \"Georgia\", \"Czech Republic\"), +     away_team = c(\"Scotland\", \"Switzerland\", \"Croatia\", \"Albania\", \"Netherlands\", \"Denmark\", \"England\", \"Ukraine\", \"Slovakia\",  +                   \"France\", \"Georgia\", \"Czech Republic\", \"Albania\", \"Hungary\", \"Switzerland\", \"Serbia\", \"England\", \"Italy\",  +                   \"Ukraine\", \"Austria\", \"France\", \"Czech Republic\", \"Portugal\", \"Romania\", \"Germany\", \"Hungary\", \"Spain\",  +                   \"Italy\", \"Austria\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Portugal\", \"Turkey\") + ) >  > # \u0420\u0430\u0441\u0447\u0435\u0442 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430 > group_stage_matches <- group_stage_matches %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_win_rate = total_win_rate, +            home_team_total_games = total_games, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_win_rate = total_win_rate, +            away_team_total_games = total_games, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > group_stage_matches[is.na(group_stage_matches)] <- 0 >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430 > predictions <- predict(rf_model, group_stage_matches[features]) > results <- ifelse(predictions == 1, \"Home Win\", ifelse(predictions == 0, \"Draw\", \"Away Win\")) >  > # \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0438 \u043f\u043e\u0434\u0441\u0447\u0435\u0442 \u043e\u0447\u043a\u043e\u0432 > group_stage_matches <- group_stage_matches %>% +     mutate(result = results, +            home_points = ifelse(result == \"Home Win\", 3, ifelse(result == \"Draw\", 1, 0)), +            away_points = ifelse(result == \"Away Win\", 3, ifelse(result == \"Draw\", 1, 0))) >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0442\u0430\u0431\u043b\u0438\u0446\u044b \u043e\u0447\u043a\u043e\u0432 > group_points <- group_stage_matches %>% +     select(home_team, home_points) %>% +     rename(team = home_team, points = home_points) %>% +     bind_rows(group_stage_matches %>% +                   select(away_team, away_points) %>% +                   rename(team = away_team, points = away_points)) %>% +     group_by(team) %>% +     summarise(total_points = sum(points)) %>% +     arrange(desc(total_points)) >  > # \u0412\u044b\u0432\u043e\u0434 \u043e\u0447\u043a\u043e\u0432 \u043a\u043e\u043c\u0430\u043d\u0434 > print(group_points) # A tibble: 24 \u00d7 2    team           total_points    <chr>                 <dbl>  1 France                    9  2 Germany                   9  3 Portugal                  9  4 Spain                     9  5 Romania                   7  6 Czech Republic            6  7 Netherlands               6  8 Scotland                  6  9 Denmark                   5 10 England                   5 # \u2139 14 more rows # \u2139 Use `print(n = ...)` to see more rows >  > # \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043a\u043e\u043c\u0430\u043d\u0434, \u0432\u044b\u0448\u0435\u0434\u0448\u0438\u0445 \u0432 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 > groups <- list( +     A = c(\"Germany\", \"Scotland\", \"Hungary\", \"Switzerland\"), +     B = c(\"Spain\", \"Croatia\", \"Italy\", \"Albania\"), +     C = c(\"Slovenia\", \"Denmark\", \"Serbia\", \"England\"), +     D = c(\"Poland\", \"Netherlands\", \"Austria\", \"France\"), +     E = c(\"Belgium\", \"Slovakia\", \"Romania\", \"Ukraine\"), +     F = c(\"Turkey\", \"Georgia\", \"Portugal\", \"Czech Republic\") + ) >  > playoff_teams <- list() > third_place_teams <- list() >  > for (group in names(groups)) { +     group_teams <- groups[[group]] +     group_points_filtered <- group_points %>% filter(team %in% group_teams) +     playoff_teams[[group]] <- group_points_filtered$team[1:2] +     third_place_teams[[group]] <- group_points_filtered$team[3] + } >  > # \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043b\u0443\u0447\u0448\u0438\u0445 \u0442\u0440\u0435\u0442\u044c\u0438\u0445 \u043c\u0435\u0441\u0442 > third_place_teams_points <- group_points %>% filter(team %in% unlist(third_place_teams)) > best_third_place_teams <- third_place_teams_points %>% arrange(desc(total_points)) %>% head(4) %>% pull(team) >  > # \u0417\u0430\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u0440\u0430\u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043c\u0430\u0442\u0447\u0435\u0439 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 > playoff_schedule <- data.frame( +     match = c(\"Match \u2116 38\", \"Match \u2116 37\", \"Match \u2116 40\", \"Match \u2116 39\", \"Match \u2116 42\", \"Match \u2116 41\", \"Match \u2116 43\", \"Match \u2116 44\"), +     home_team = c(playoff_teams$A[2], playoff_teams$A[1], playoff_teams$C[1], playoff_teams$B[1], playoff_teams$D[2], playoff_teams$F[1], playoff_teams$E[1], playoff_teams$D[1]), +     away_team = c(playoff_teams$B[2], playoff_teams$C[2], best_third_place_teams[1], best_third_place_teams[2], playoff_teams$E[2], best_third_place_teams[3], best_third_place_teams[4], playoff_teams$F[2]) + ) >  > print(playoff_schedule)<\/code><\/pre>\n
1\/8 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444:<\/p>\n
\n
\n\n\n\n\n\n\n\n\n\n\n
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Match \u2116 38<\/p>\n<\/td>\n
\n
Scotland<\/p>\n<\/td>\n
\n
Croatia<\/p>\n<\/td>\n<\/tr>\n
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Match \u2116 37<\/p>\n<\/td>\n
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Germany<\/p>\n<\/td>\n
\n
England<\/p>\n<\/td>\n<\/tr>\n
\n
Match \u2116 40<\/p>\n<\/td>\n
\n
Denmark<\/p>\n<\/td>\n
\n
Italy<\/p>\n<\/td>\n<\/tr>\n
\n
Match \u2116 39<\/p>\n<\/td>\n
\n
Spain<\/p>\n<\/td>\n
\n
Slovenia<\/p>\n<\/td>\n<\/tr>\n
\n
Match \u2116 42<\/p>\n<\/td>\n
\n
Netherlands<\/p>\n<\/td>\n
\n
Belgium<\/p>\n<\/td>\n<\/tr>\n
\n
Match \u2116 41<\/p>\n<\/td>\n
\n
Portugal<\/p>\n<\/td>\n
\n
Hungary<\/p>\n<\/td>\n<\/tr>\n
\n
Match \u2116 43<\/p>\n<\/td>\n
\n
Romania<\/p>\n<\/td>\n
\n
Poland<\/p>\n<\/td>\n<\/tr>\n
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Match \u2116 44<\/p>\n<\/td>\n
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France<\/p>\n<\/td>\n
\n
Czech   Republic<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
\u041d\u0430 \u044d\u0442\u043e\u043c \u0437\u0430\u0432\u0435\u0440\u0448\u0430\u0435\u0442\u0441\u044f \u043f\u0435\u0440\u0432\u044b\u0439 \u044d\u0442\u0430\u043f \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f. <\/p>\n
\u041d\u0430 \u0432\u0442\u043e\u0440\u043e\u043c \u044d\u0442\u0430\u043f\u0435 \u044f \u043f\u043e\u0434\u0432\u0435\u0434\u0443 \u043f\u0440\u043e\u043c\u0435\u0436\u0443\u0442\u043e\u0447\u043d\u044b\u0435 \u0438\u0442\u043e\u0433\u0438 \u0438 \u0434\u0430\u043c \u043f\u0440\u043e\u0433\u043d\u043e\u0437 \u043d\u0430 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0432\u0448\u0438\u0445\u0441\u044f \u043f\u0430\u0440 \u0432 1\/8. <\/p>\n
\u041d\u0430 \u0442\u0440\u0435\u0442\u044c\u0435\u043c \u044d\u0442\u0430\u043f\u0435 \u043f\u043e\u0434\u0432\u0435\u0434\u0443 \u043e\u0431\u0449\u0438\u0435 \u0438\u0442\u043e\u0433\u0438.<\/p>\n
\u00a0<\/p>\n
\u041e\u0446\u0435\u043d\u043a\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f:<\/p>\n
1) \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0431\u044b\u043b\u043e \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043e \u0432\u0435\u0440\u043d\u043e \u0438 \u0441\u0440\u0430\u0432\u043d\u0438\u043c \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0441 57.65. \u0422\u0430\u043a \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u043c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u0435\u0440\u043d\u043e \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440 \u043e\u0446\u0435\u043d\u0438\u043b \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0441\u0432\u043e\u0435\u0433\u043e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430. <\/p>\n
2) \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0439 \u0431\u0430\u043d\u043a \u043f\u043e\u0441\u043b\u0435 \u0442\u0443\u0440\u043d\u0438\u0440\u0430 \u0438 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u043c, \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u0435 \u043e\u0431\u044b\u0433\u0440\u0430\u0442\u044c \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430. <\/p>\n
\u00a0<\/p>\n
\u0412\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0439 \u0431\u0430\u043d\u043a<\/p>\n
\u0414\u043b\u044f \u0442\u043e\u0433\u043e \u0447\u0442\u043e\u0431\u044b \u0443\u0437\u043d\u0430\u0442\u044c, \u043f\u0440\u0438\u043d\u0435\u0441\u0435\u0442 \u043d\u0430\u043c \u043f\u0440\u0438\u0431\u044b\u043b\u044c \u0438\u043b\u0438 \u0443\u0431\u044b\u0442\u043e\u043a \u0432 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u043e\u0439 \u043a\u043e\u043d\u0442\u043e\u0440\u0435 \u0442\u0430\u043a\u0430\u044f \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f, \u043c\u044b \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0439 \u0431\u0430\u043d\u043a \u0432 \u0440\u0430\u0437\u043c\u0435\u0440\u0435 5 300 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432. 51 \u043c\u0430\u0442\u0447 \u0431\u0443\u0434\u0435\u0442 \u0441\u044b\u0433\u0440\u0430\u043d \u043d\u0430 \u044d\u0442\u043e\u043c \u0442\u0443\u0440\u043d\u0438\u0440\u0435, \u043d\u0430 \u043a\u0430\u0436\u0434\u044b\u0439 \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0430 \u0443\u0441\u043b\u043e\u0432\u043d\u0430\u044f \u0441\u0442\u0430\u0432\u043a\u0430 \u0432 \u0440\u0430\u0437\u043c\u0435\u0440\u0435 100 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u043c\u0430\u0448\u0438\u043d\u044b + 2 \u0440\u0430\u0437\u0430 \u043f\u043e 100 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432 \u043c\u044b \u043f\u043e\u0441\u0442\u0430\u0432\u0438\u043c \u043d\u0430 \u0447\u0435\u043c\u043f\u0438\u043e\u043d\u0430 — \u0434\u043e \u043d\u0430\u0447\u0430\u043b\u0430 \u0442\u0443\u0440\u043d\u0438\u0440\u0430 \u0438 \u043f\u043e\u0441\u043b\u0435 \u043e\u043a\u043e\u043d\u0447\u0430\u043d\u0438\u044f \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0439 \u0441\u0442\u0430\u0434\u0438\u0438. <\/p>\n
\u042f \u0431\u0443\u0434\u0443 \u0431\u0440\u0430\u0442\u044c \u0441\u0440\u0435\u0434\u043d\u0438\u0439 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 \u043d\u0430 \u0441\u0430\u0439\u0442\u0435 https:\/\/www.flashscore.com.ua\/<\/a>, \u0447\u0442\u043e\u0431\u044b \u043d\u0435 \u0440\u0435\u043a\u043b\u0430\u043c\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u043a\u043e\u0433\u043e-\u0442\u043e \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430.<\/p>\n
\u00a0<\/p>\n
\u0410 \u0447\u0435\u043c\u043f\u0438\u043e\u043d\u043e\u043c \u0415\u0432\u0440\u043e 2024 \u043f\u043e \u0432\u0435\u0440\u0441\u0438\u0438 \u043c\u0430\u0448\u0438\u043d\u044b \u0431\u0443\u0434\u0435\u0442 \u0418\u0441\u043f\u0430\u043d\u0438\u044f.<\/p>\n<\/div>\n<\/div>\n<\/div>\n
<\/div>\n

 \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438  https:\/\/habr.com\/ru\/articles\/821133\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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\n
\u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440<\/strong><\/p>\n
\u041f\u043e \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u043d\u0438\u0436\u0435 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0442\u0430\u0432\u043a\u0438, \u0442.\u043a. \u043e\u043d\u0438 \u043d\u0435 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044e\u0442 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u0443\u044e \u043c\u0430\u0440\u0436\u0443, \u0444\u043e\u0440\u043c\u0443 \u043a\u043e\u043c\u0430\u043d\u0434, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043c\u043d\u043e\u0433\u043e \u0434\u0440\u0443\u0433\u0438\u0445 \u0444\u0430\u043a\u0442\u043e\u0440\u043e\u0432. \u0412 \u0446\u0435\u043b\u043e\u043c \u0441\u0442\u0430\u0432\u043a\u0438 \u043d\u0430 \u0441\u043f\u043e\u0440\u0442 \u0432\u0435\u0441\u044c\u043c\u0430 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u0447\u043d\u043e\u0435 \u0437\u0430\u043d\u044f\u0442\u0438\u0435, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u043e\u0435 \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u043d\u0430 \u043f\u0441\u0438\u0445\u043e\u043b\u043e\u0433\u0438\u044e \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430, \u0441\u043a\u0440\u044b\u0442\u044b\u0435 \u0441\u043b\u0430\u0431\u043e\u0441\u0442\u0438 \u0438 \u0442. \u0434., \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0432 \u0446\u0435\u043b\u043e\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043b\u044e\u0431\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435. <\/p>\n<\/p>\n
\u0414\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u044f \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0438\u0441\u044c: \u0434\u0430\u0442\u0430\u0441\u0435\u0442: https:\/\/www.kaggle.com\/datasets\/martj42\/international-football-results-from-1872-to-2017?resource=download<\/a> , \u044f\u0437\u044b\u043a \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f R, \u0447\u0430\u0442 GPT 4.0.\u00a0<\/p>\n
\u0426\u0435\u043b\u0438 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: 1) \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u0431\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445 \u0437\u0430 20 \u043b\u0435\u0442 2) \u0443\u0437\u043d\u0430\u0442\u044c \u0440\u0430\u0437\u043c\u0435\u0440 \u0432\u044b\u0438\u0433\u0440\u044b\u0448\u0430\/\u043f\u0440\u043e\u0438\u0433\u0440\u044b\u0448\u0430 \u0432 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u043e\u0439 \u043a\u043e\u043d\u0442\u043e\u0440\u0435 \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430. <\/p>\n
\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0430\u0442\u0438\u043a\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f: \u0437\u0434\u0435\u0441\u044c \u043d\u0435 \u0443\u0447\u0442\u0435\u043d\u044b \u0432\u0430\u0436\u043d\u044b\u0435 \u0444\u0430\u043a\u0442\u043e\u0440\u044b \u0442\u0430\u043a\u0438\u0435 \u043a\u0430\u043a \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0438 \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u0438\u0433\u0440\u043e\u043a\u043e\u0432, \u0442\u0435\u043a\u0443\u0449\u0430\u044f \u0444\u043e\u0440\u043c\u0430 \u043a\u043e\u043c\u0430\u043d\u0434, \u0444\u0430\u043a\u0442\u043e\u0440 \u0434\u043e\u043c\u0430\u0448\u043d\u0435\u0433\u043e \u0442\u0443\u0440\u043d\u0438\u0440\u0430 \u0434\u043b\u044f \u0441\u0431\u043e\u0440\u043d\u043e\u0439 \u0413\u0435\u0440\u043c\u0430\u043d\u0438\u0438 \u0438 \u043c\u043d\u043e\u0433\u043e\u0435 \u0434\u0440\u0443\u0433\u043e\u0435.<\/p>\n
\u042d\u0442\u043e \u0432\u0441\u0435 \u0443\u0447\u0442\u0435\u043d\u043e \u0441\u0430\u043c\u0438\u043c\u0438 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430\u043c\u0438 \u043f\u0440\u0438 \u0432\u044b\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432 \u043d\u0430 \u043c\u0430\u0442\u0447\u0438, \u043c\u0438\u043d\u0443\u0441 10-15 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u0438\u0445 \u043c\u0430\u0440\u0436\u0438, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u044b\u0431\u0438\u0440\u0430\u044f \u0444\u0430\u0432\u043e\u0440\u0438\u0442\u043e\u0432 \u0432\u044b\u0438\u0433\u0440\u0430\u0442\u044c \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e. <\/p>\n
\u041b\u0438\u0447\u043d\u043e \u0434\u043b\u044f \u043c\u0435\u043d\u044f \u0431\u043e\u043b\u044c\u0448\u0438\u0439 \u0438\u043d\u0442\u0435\u0440\u0435\u0441 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u043e\u0442\u0432\u0435\u0442 \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441 — \u0443\u0434\u0430\u0441\u0442\u0441\u044f \u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u0435 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0442\u044c \u043d\u0435\u043e\u0447\u0435\u0432\u0438\u0434\u043d\u044b\u0435 \u0437\u0430\u043a\u043e\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u0438 \u0438 \u043e\u0431\u044b\u0433\u0440\u0430\u0442\u044c \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0430, \u0430 \u043d\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0444\u0430\u0432\u043e\u0440\u0438\u0442\u0430. <\/p>\n
\u00a0<\/p>\n
\u041c\u0435\u0442\u043e\u0434\u043e\u043b\u043e\u0433\u0438\u044f <\/p>\n
\u0412 \u043f\u0435\u0440\u0432\u0443\u044e \u043e\u0447\u0435\u0440\u0435\u0434\u044c \u0431\u044b\u043b \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u0430\u0442\u0430\u0441\u0435\u0442, \u0442\u0430\u043a \u043a\u0430\u043a \u043e\u043d \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0431\u043e\u043b\u0435\u0435 47 000 \u043c\u0430\u0442\u0447\u0435\u0439 \u0437\u0430 152 \u0433\u043e\u0434\u0430, \u0432 \u0442\u043e\u043c \u0447\u0438\u0441\u043b\u0435 — \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u0444\u0440\u0438\u043a\u0430\u043d\u0441\u043a\u0438\u0445 \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u043c \u043d\u0435 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u044b \u0438 \u0437\u0430\u043c\u0435\u0434\u043b\u0438\u043b\u0438 \u0431\u044b \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0431\u044b\u043b \u0441\u043e\u043a\u0440\u0430\u0449\u0435\u043d \u0434\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0435\u0432\u0440\u043e, \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u043a \u043d\u0435\u043c\u0443 \u0438 \u043b\u0438\u0433\u0435 \u043d\u0430\u0446\u0438\u0439. <\/p>\n
*\u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044e \u043a \u043c\u0438\u0440\u0443 \u0431\u0440\u0430\u0442\u044c \u043d\u0435 \u0441\u0442\u0430\u043b, \u0442.\u043a. \u0445\u043e\u0442\u044f \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0442\u0435 \u0436\u0435, \u044d\u0442\u043e \u0434\u0440\u0443\u0433\u043e\u0439 \u0442\u0443\u0440\u043d\u0438\u0440 \u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u043e\u0442\u043b\u0438\u0447\u0430\u0435\u0442\u0441\u044f*<\/em><\/p>\n
\u0417\u0430 \u0442\u043e\u0447\u043a\u0443 \u043e\u0442\u0441\u0447\u0451\u0442\u0430 \u0432\u0437\u044f\u0442 \u0435\u0432\u0440\u043e 1996 \u0433\u043e\u0434\u0430 \u0438 \u043a\u0430\u043a \u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u0435 \u043a\u0432\u0430\u043b\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u043a \u043d\u0435\u043c\u0443, \u043d\u0430\u0447\u0438\u043d\u0430\u044f \u0441 1994 \u0433\u043e\u0434\u0430. \u0422\u0430\u043a\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0441\u0432\u044f\u0437\u0430\u043d\u043e \u0441 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0430 \u0442\u0443\u0440\u043d\u0438\u0440\u0430, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0441 \u0440\u0430\u0437\u0432\u0430\u043b\u043e\u043c \u0441\u0442\u0440\u0430\u043d \u0441\u043e\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0431\u043b\u043e\u043a\u0430 (\u0443\u0432\u0435\u043b\u0438\u0447\u0438\u043b\u043e\u0441\u044c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u0442\u0440\u0430\u043d \u0443\u0447\u0430\u0441\u0442\u043d\u0438\u0446). <\/p>\n
\u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043c\u044b \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u043e\u0434\u0438\u043d \u0441\u043e\u0441\u0442\u0430\u0432 \u0443\u0447\u0430\u0441\u0442\u043d\u0438\u043a\u043e\u0432 \u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0437\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 20 \u043b\u0435\u0442. \u0424\u0438\u043d\u0430\u043b\u044c\u043d\u044b\u0439 \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u043b 2 758 \u043c\u0430\u0442\u0447\u0435\u0439. <\/p>\n
\u00a0<\/p>\n
\u0414\u0430\u043b\u0435\u0435 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0447\u0430\u0442\u0430 GPT \u044f \u043f\u0435\u0440\u0435\u0431\u0440\u0430\u043b \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0432 Python (\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b: pandas, numpy, train_test_split, GridSearchCV, RandomForestClassifier, accuracy_score). <\/p>\n
\u041b\u0443\u0447\u0448\u0438\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u0441\u0442\u0430\u043b\u0430 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 — 53.51%.<\/p>\n
\u0422\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u043e\u0441\u044c \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u044f\u0437\u044b\u043a R.<\/p>\n
\u041b\u0443\u0447\u0448\u0438\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u043d\u0430 R \u0441\u0442\u0430\u043b\u0430 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 — 57.65%<\/p>\n
\u0412\u0435\u0441\u044c\u043c\u0430 \u043d\u0435\u043f\u043b\u043e\u0445\u043e\u0439 \u043f\u0440\u043e\u0446\u0435\u043d\u0442, \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044f, \u0447\u0442\u043e \u0438\u0433\u0440\u0430 \u0438\u0434\u0435\u0442 \u043d\u0430 3 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430. \u0422\u0430\u043a \u043a\u0430\u043a \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u043d\u0430 R \u0443 \u043d\u0430\u0441 \u0432\u044b\u0448\u0435, \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0435\u0433\u043e \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.<\/p>\n
> library(randomForest) > library(dplyr) >  > # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 > data <- read.csv(\"filtered_results.csv\") >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b > data$date <- as.Date(data$date, format=\"%Y-%m-%d\") >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 > data$result <- ifelse(data$home_score > data$away_score, 1,  +                       ifelse(data$home_score < data$away_score, -1, 0)) >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 > home_games <- data %>% +     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result) >  > away_games <- data %>% +     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>% +     mutate(result = ifelse(result == 1, -1, ifelse(result == -1, 1, 0))) >  > all_games <- bind_rows(home_games, away_games) >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 > team_stats <- all_games %>% +     group_by(team) %>% +     summarise(total_games = n(), +               total_win_rate = mean(result == 1), +               total_avg_score = mean(score)) >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > data <- data %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_games = total_games, +            home_team_total_win_rate = total_win_rate, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_games = total_games, +            away_team_total_win_rate = total_win_rate, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > data[is.na(data)] <- 0 >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > features <- c(\"home_team_total_win_rate\", \"away_team_total_win_rate\",  +               \"home_team_total_games\", \"away_team_total_games\",  +               \"home_team_total_avg_score\", \"away_team_total_avg_score\") > X <- data[features] > y <- factor(data$result) >  > # \u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438 > set.seed(42) > train_indices <- sample(seq_len(nrow(data)), size = 0.8 * nrow(data)) > X_train <- X[train_indices, ] > y_train <- y[train_indices] > X_test <- X[-train_indices, ] > y_test <- y[-train_indices] >  > # \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 Random Forest > rf_model <- randomForest(X_train, y_train, ntree=200, mtry=3, importance=TRUE) >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435 > y_pred <- predict(rf_model, X_test) > accuracy <- sum(y_pred == y_test) \/ length(y_test) > print(paste(\"Accuracy:\", accuracy)) [1] \"Accuracy: 0.576576576576577\" >  > # \u041f\u0440\u0438\u043c\u0435\u0440 \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > new_matches <- data.frame( +     home_team = c(\"Germany\", \"Hungary\", \"Spain\", \"Italy\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Austria\",  +                   \"Turkey\", \"Portugal\", \"Croatia\", \"Germany\", \"Scotland\", \"Slovenia\", \"Denmark\", \"Spain\", \"Slovakia\",  +                   \"Poland\", \"Netherlands\", \"Georgia\", \"Turkey\", \"Belgium\", \"Switzerland\", \"Scotland\", \"Albania\", \"Croatia\",  +                   \"Netherlands\", \"France\", \"England\", \"Denmark\", \"Slovakia\", \"Ukraine\", \"Georgia\", \"Czech Republic\"), +     away_team = c(\"Scotland\", \"Switzerland\", \"Croatia\", \"Albania\", \"Netherlands\", \"Denmark\", \"England\", \"Ukraine\", \"Slovakia\",  +                   \"France\", \"Georgia\", \"Czech Republic\", \"Albania\", \"Hungary\", \"Switzerland\", \"Serbia\", \"England\", \"Italy\",  +                   \"Ukraine\", \"Austria\", \"France\", \"Czech Republic\", \"Portugal\", \"Romania\", \"Germany\", \"Hungary\", \"Spain\",  +                   \"Italy\", \"Austria\", \"Poland\", \"Slovenia\", \"Serbia\", \"Romania\", \"Belgium\", \"Portugal\", \"Turkey\") + ) >  > # \u0420\u0430\u0441\u0447\u0435\u0442 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0434\u043b\u044f \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > new_matches <- new_matches %>% +     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>% +     rename(home_team_total_win_rate = total_win_rate, +            home_team_total_games = total_games, +            home_team_total_avg_score = total_avg_score) %>% +     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>% +     rename(away_team_total_win_rate = total_win_rate, +            away_team_total_games = total_games, +            away_team_total_avg_score = total_avg_score) >  > # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 > new_matches[is.na(new_matches)] <- 0 >  > # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043d\u043e\u0432\u044b\u0445 \u043c\u0430\u0442\u0447\u0435\u0439 > predictions <- predict(rf_model, new_matches[features]) > results <- ifelse(predictions == 1, \"Home Win\", ifelse(predictions == 0, \"Draw\", \"Away Win\")) >  > # \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 > for (i in 1:nrow(new_matches)) { +     print(paste(new_matches$home_team[i], \"vs\", new_matches$away_team[i], \"-> Prediction:\", results[i]))<\/code><\/pre>\n
\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430:<\/p>\n
1. Germany vs Scotland -> Prediction: Home Win<\/p>\n
2. Hungary vs Switzerland -> Prediction: Home Win<\/p>\n
3. Spain vs Croatia -> Prediction: Home Win<\/p>\n
4. Italy vs Albania -> Prediction: Home Win<\/p>\n
5. Poland vs Netherlands -> Prediction: Away Win<\/p>\n
6. Slovenia vs Denmark -> Prediction: Draw<\/p>\n
7. Serbia vs England -> Prediction: Draw<\/p>\n
8. Romania vs Ukraine -> Prediction: Home Win<\/p>\n
9. Belgium vs Slovakia -> Prediction: Home Win<\/p>\n
10. Austria vs France -> Prediction: Away Win<\/p>\n
11. Turkey vs Georgia -> Prediction: Home Win<\/p>\n
12. Portugal vs Czech Republic -> Prediction: Home Win<\/p>\n
13. Croatia vs Albania -> Prediction: Home Win<\/p>\n
14. Germany vs Hungary -> Prediction: Home Win<\/p>\n
15. Scotland vs Switzerland -> Prediction: Home Win<\/p>\n
16. Slovenia vs Serbia -> Prediction: Home Win<\/p>\n
17. Denmark vs England -> Prediction: Draw<\/p>\n
18. Spain vs Italy -> Prediction: Home Win<\/p>\n
19. Slovakia vs Ukraine -> Prediction: Home Win<\/p>\n
20. Poland vs Austria -> Prediction: Home Win<\/p>\n
21. Netherlands vs France -> Prediction: Away Win<\/p>\n
22. Georgia vs Czech Republic -> Prediction: Away Win<\/p>\n
23. Turkey vs Portugal -> Prediction: Away Win<\/p>\n
24. Belgium vs Romania -> Prediction: Draw<\/p>\n
25. Switzerland vs Germany -> Prediction: Away Win<\/p>\n
26. Scotland vs Hungary -> Prediction: Home Win<\/p>\n
27. Albania vs Spain -> Prediction: Away Win<\/p>\n
28. Croatia vs Italy -> Prediction: Draw<\/p>\n
29. Netherlands vs Austria -> Prediction: Home Win<\/p>\n
30. France vs Poland -> Prediction: Home Win<\/p>\n
31. England vs Slovenia -> Prediction: Home Win<\/p>\n
32. Denmark vs Serbia -> Prediction: Home Win<\/p>\n
33. Slovakia vs Romania -> Prediction: Away Win<\/p>\n
34. Ukraine vs Belgium -> Prediction: Home Win<\/p>\n
35. Georgia vs Portugal -> Prediction: Away Win<\/p>\n
36. Czech Republic vs Turkey -> Prediction: Home Win<\/p>\n
\u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0441\u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043b\u0430\u0441\u044c \u0441\u0435\u0442\u043a\u0430 1\/8 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043c\u0430\u0442\u0447\u0435\u0439. <\/p>\n
library(randomForest) > library(dplyr) >  > # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 > data <- read.csv(\"filtered_results.csv\") >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b > data$date <- as.Date(data$date, format=\"%Y-%m-%d\") >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 > data$result <- ifelse(data$home_score > data$away_score, 1,  +                       ifelse(data$home_score < data$away_score, -1, 0)) >  > # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 > home_games <- data %>% +     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result) >  > away_games <- data %>% +     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>% +     mutate(result = ifelse(result == 1, -1, ifelse(result == -1, 1, 0))) >  > all_games <- bind_rows(home_games, away_games) >  > # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 > team_stats <- all_games %>% +     group_by(team) %>% +     summarise(total_games = n(), +               total_win_rate = mean(result == 1), +               total_avg_score = mean(score)) >  > # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 > data <- data %>% +  <\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-378644","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/378644","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=378644"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/378644\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=378644"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=378644"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=378644"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}