基于机器学习算法的电子商务市场增长预测研究

Naved Kalal, Sameer Dhanawale, R. Ghadge, Kulwantsinh Nimbalkar, Madhuri K. Gawali
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引用次数: 2

摘要

学习是充分执行想法的关键。机器学习使IT组织能够在当前可用的算法和数据框架的基础上识别模式,以培养可接受的解决方案概念。在线商业市场和客户保留是一种关系,就像硬币的两面。这是一个非线性关系。随着电子商务市场的发展,业务增长预测是一个非常敏感的问题。商业市场的在线供应商以虚拟预测为基础进行库存管理,以满足客户需求-供应链的基本需求。授权传统的方法和分析方法不能保证销售预测的可靠性。为了产生更精确的预测和分析,我们使用ML算法。在本文中,我们利用某电子商务公司的销售数据集,并将其分离,在不同的季度中计算每个季度的销售收入。然后我们将数据集按照70%和30%的比例划分为训练数据集和测试数据集。通过应用机器学习算法,我们将预测下个季度的收入,并分析每个季度最大销售的商品及其购买频率。然后将客户购买模式的分析结果和预测提供给商业组织,为其商品管理和库存规划制定持续积累的竞争优势策略。
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Study for the Prediction of E-Commerce Business Market Growth using Machine Learning Algorithm
Learning is a key to perform ideas adequately. Machine Learning empowers IT organizations to identify the patterns on the basis of currently available algorithms and data frames to cultivate acceptable solution concepts. Online business market and customer retention is a relation like the two sides of a coin. It is a nonlinear relationship. Prediction of Business growth is a very sensitive issue of E-Commerce market with its future existence. Online venders of business market manage their inventories on virtual prediction bases for full filling the basic need of demand-supply chain of customers. Authorizing traditional ways and analysis methods are not ensuring the rate of reliability of the sales prediction. To produce more precise predictions and analysis, we use ML algorithm. In this paper, we utilized the selling data set of an E-commerce company and segregated it, in different quarters then calculating the sale income per quarter. After that we divided the dataset in the proportion of 70% and 30% for Training data set and Testing data set. By applying machine learning algorithm, we will be predicting income of next quarters as well as analysis the maximally sold commodities with their frequencies of purchase per quarter. Then provide analysis results and prediction of customer's purchase patterns to the business organization to make a strategy to take a competitive advantage by sustaining and accumulating for their goods management and planning for inventories.
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