COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

Rabia Özdemir, M. Turanli
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引用次数: 4

Abstract

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naive Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio.
采购预测中机器学习分类算法的比较
随着计算机技术的发展和互联网的发明,许多概念进入了我们的生活。随着全球化互联网网络的广泛使用,机器学习的概念应运而生,可以对大维度的数据流进行更智能的管理。随着科技的发展,所有的活动都开始进入数字环境,电子商务的概念也因此进入了我们的生活。电子商务是机器学习应用最广泛的领域之一。根据企业现有的数据,考察产品采购情况,选择最合适的模型进行各种研究,预测未来的数据。在研究中提到了电子商务和机器学习的概念,并通过应用逻辑回归,朴素贝叶斯和支持向量机作为机器学习分类算法,旨在确定具有最佳准确率的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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