Performance Analysis of and Neural KNN Networks for Predicting Customer Purchases in a Real Retail Department Store

Ledion Liço, I. Enesi
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Abstract

Customer Relationship Management technology plays an important role in business performance. Predicting customer behavior enables the business to better address their customers and enhance service level and overall profit. The aim of this paper is to create models that classify clients and predict their purchases in a real retail department store. A real department store retail transactions dataset will be used and two classification/regression models will be tested on it. The first is based on K-Nearest Neighbors and the other one is based on Neural Networks.
真实零售百货商店顾客购买预测的性能分析及神经KNN网络
客户关系管理技术在企业绩效中起着重要的作用。预测客户行为使企业能够更好地解决他们的客户,提高服务水平和整体利润。本文的目的是创建对客户进行分类并预测其在真实零售百货商店中的购买行为的模型。将使用一个真实的百货商店零售交易数据集,并在其上测试两个分类/回归模型。第一种是基于k近邻,另一种是基于神经网络。
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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