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如何运用机器学习预测客户对保险的兴趣
保险公司可以通过订阅的健康保险客户数据集利用Python库、matplotlib和Seaborn进行数据可视化,以确定哪些客户有兴趣购买车辆保险。机器学习在预测和分析方面有广泛的应用,可以帮助公司制定更有效的销售和服务策略,优化业务模型。此外,我们还可以采用机器学习来改善服务和客户保留,以目标为导向
Python 7 0 zip 2023-04-15 01:04:38
健康保险与车辆保险交叉销售预测
针对车辆保险的潜在客户,保险公司需要预测健康保险客户是否会对车辆保险产生兴趣。为此,我们建立了一个预测模型。我们的目标是优化业务模式并提高收入,通过相应的规划沟通策略来接触到这些潜在客户。我们使用来自Kaggle的数据集进行分析,数据包含客户的个人信息和车辆保险相关信息。该模型的成功将有助于为保险公
Python 14 0 zip 2023-04-15 01:04:25
预测保险客户是否购买车险
我们的客户是一家保险公司,他们已经向客户提供了健康保险,现在他们需要您的帮助建立一个模型,预测过去一年的保险持有人(客户)是否也对该公司提供的车辆保险感兴趣。结论1.上面的表格是所有算法在超参数调整之后的评估矩阵,我们将更注重召回率而不是准确性,因为我们想减少假负数(实际存在而预测不存在的客户)。
Python 22 0 zip 2023-03-29 10:03:09
预测客户是否购买车险
我们的客户是一家保险公司,他们正在寻求建立一个预测模型,以预测去年的保单持有人是否也对公司提供的车险感兴趣。数据说明:id:客户的唯一ID;Gender:客户的性别;Age:客户的年龄;Driving_License:0:客户没有驾照,1:客户已经拥有驾照。
Python 23 0 zip 2023-03-29 10:03:37
Predictive Analysis for Health and Vehicle Insurance Cross Selling
The client, an insurance company, is seeking assistance in building a predictive model to determine if health insurance customers from the previous ye
Python 18 0 zip 2023-03-29 10:03:17
Predicting Customer Interest in Vehicle Insurance for Health Insurance Customers
As an insurance company that has provided health insurance to its customers, it is essential to predict whether or not they will be interested in vehi
Python 26 0 zip 2023-03-29 10:03:03
How to Predict Health Insurance Customers Interest in Vehicle Insurance
Our client, an Insurance company, has approached us for assistance in developing a model for predicting their past year’s policyholders' interest in t
Python 35 0 zip 2023-03-29 10:03:06
Predicting Health Insurance Customers Interest in Vehicle Coverage
Our client, a top-tier insurance provider, is seeking assistance with predicting their current health insurance policyholders' likelihood of purchasin
Python 24 0 zip 2023-03-29 10:03:22
Predictive Modeling for Cross Sell of Health Insurance
This article discusses the importance of predictive modeling in identifying potential customers for health insurance cross-selling. We explain the dif
Python 29 0 zip 2023-03-29 09:03:52