The income prediction module of the retail store's network
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URI (для ссылок/цитирований):
http://amsa.conf.nstu.ru/amsa2019/proceedings/AMSA2019-proceedings.pdfhttps://elib.sfu-kras.ru/handle/2311/128609
Автор:
Kristina, Pakhomova
Pavel, Peresunko
Sergey, Videnin
Eugenia, Soroka
Коллективный автор:
Институт космических и информационных технологий
Кафедра информатики
Кафедра информационных систем
Дата:
2019-09Журнал:
Applied Methods of Statistical AnalysisКвартиль журнала в Scopus:
без квартиляБиблиографическое описание:
Kristina, Pakhomova. The income prediction module of the retail store's network [Текст] / Pakhomova Kristina, Peresunko Pavel, Videnin Sergey, Soroka Eugenia // Applied Methods of Statistical Analysis: Statistical Computation and Simulation. — 2019. — Т. 1 (№ 5).Аннотация:
The main idea of this paper focused on the development of a program module, which predicts the pharmacy retail income by the machine learning theory.
Beyond that, we want to introduce the best prediction model, which has learned
by speci c retail dataset. Notice, the architecture of program involves dynamic
upload dataset, by Yandex" Internet service. The dataset represents the set of
features and set of retail points, however in this task, the features describe the
pharmacy industry. So on the rst step will analyze the dataset and found out
the correlation of the features. Next, will select the relevant features, which
a ect on income rate of the retail point. The last one will introduce to the
prediction income Average model. In the last, will compare the three models, there is Average model, Gradient Boosting Regression and Random Forest
Regression