Analysis of Drug Sales Data based on Machine Learning Methods

被引：0

作者：

Al-Gunaid, Mohammed A. ^{[1
]}

Shcherbakov, Maxim V. ^{[1
]}

Kravets, Alla G. ^{[1
]}

Loshmanov, Vadim I. ^{[1
]}

Shumkin, Alexandr M. ^{[1
]}

Trubitsin, Vladislav V. ^{[1
]}

Vakulenko, Darya V. ^{[1
]}

机构：

[1] Volgograd State Tech Univ, Volgograd, Russia

来源：

PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART) | 2018年

关键词：

Forecasting; Medications; Neural Network; Linear Regression; Random Forest; Factor Analysis; Support Vector Method; Levenberg-Marquardt Algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Currently, a data analysis is inalienable part of basic processes of any big company. Building of model of some process allows forecasting its behavior very accurate in specified conditions. It enables to avoid adverse consequences in case of risks. There are a lot of software packages that implement machine learning methods for forecasting models building exist. The most common and efficient are linear regression, random forest and artificial neural networks. The aim of this article is to compare the most known forecasting methods by building data analysis models of medications' sales.

引用

页码：32 / 38

页数：7

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