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| 基于高斯朴素贝叶斯的保险产品分析 摘 要：在高速发展的21世纪，计算机已经成为了推动社会进步中不可或缺的一种手段，它代替大量人力，并且可以高效的完成以往人类认知中不可相信的任务。 目前我国的保险产品多达上万种，保险公司为了尽可能的吸引用户购买他们所需要的保险，非常细致的划分了各个保险产品的类别以及用途，保险产品的多样性、客户群体的特征的复杂性以及需求差异使得保险产品推荐成为了热门话题，保险产品推荐需要考虑保险产品本身的特点和用户的多方面特征，再根据有限的行为数据挖掘用户的偏好，使用python语言，通过朴素贝叶斯分类器中的高斯朴素贝叶斯对用户本身属性和过往保险购买记录分析客户特点，对广大用户进行个人信息的有效筛选，从购买保险的用户群体中提取共同的特征，从而针对这些特征规律提高投放精准性，促进保险公司的业务发展。 本文主要介绍了，将训练集和测试集数据先进行数据预处理，包括描述性统计，卡方检验等数据分析，也可叫探索性分析还有特征工程中的onehot处理，使无法计算的数据变为可计算的0和1，之后运用sklearn库里的高斯朴素贝叶斯分类器生成模型，打印出其分析报告，再根据客户的特征数据判断出该不该给客户推荐保险。 关键词：计算机，python，高斯朴素贝叶斯，保险产品，卡方检验 Analysis of insurance products based on Gauss naive Bayes Abstract: In the rapid development of the 21st century, computer has become an indispensable means to promote social progress, it can replace a large number of human, and can efficiently complete the past human cognitive tasks that can not be believed. At present, there are tens of thousands of insurance products in China. In order to attract users to buy the insurance they need, insurance companies divide the categories and uses of various insurance products in detail. The diversity of insurance products, the complexity of customer groups and the differences in demand make insurance product recommendation a hot topic. Insurance product recommendation needs to consider insurance According to the characteristics of the product itself and the various characteristics of the users, the user preferences are mined according to the limited behavior data. Using Python language, the Gaussian naive Bayes in the naive Bayes classifier is used to analyze the customer characteristics of the user's own attributes and the past insurance purchase records, so as to effectively screen the personal information of the majority of users, and extract common information from the user groups who purchase insurance So as to improve the accuracy of delivery and promote the business development of insurance companies. Keywords: Computer, python, Gauss naive Bayes, insurance products，Chi square test |
