摘要：本文基于大数据技术，构建了一种消费者购物行为预测模型，并通过实证分析验证了该模型的有效性。首先，通过对大量的消费者购物数据进行分析，提取出了与购物行为相关的多个特征变量，包括消费者的年龄、性别、职业、收入、购物频率、购物金额等。然后，利用机器学习算法对这些特征变量进行建模，构建了一个基于逻辑回归的预测模型。最后，通过对该模型进行实证分析，发现该模型的预测准确率较高，可以有效地预测消费者的购物行为。
关键词：大数据；消费者购物行为；预测模型；机器学习算法；逻辑回归
Abstract: Based on big data technology, this paper constructs a consumer shopping behavior prediction model and verifies the effectiveness of the model through empirical analysis. Firstly, by analyzing a large amount of consumer shopping data, multiple feature variables related to shopping behavior are extracted, including consumer age, gender, occupation, income, shopping frequency, shopping amount, etc. Then, machine learning algorithms are used to model these feature variables, and a prediction model based on logistic regression is constructed. Finally, through empirical analysis of the model, it is found that the prediction accuracy of the model is high, and it can effectively predict consumer shopping behavior.
Keywords: big data; consumer shopping behavior; prediction model; machine learning algorithm; logistic regression
基于大数据技术的消费者购物行为预测模型构建及实证分析
