基于数据挖掘和K-Means模型的金融数据可视化分析

Visualization analysis of financial data based on data mining and K-Means model

在普惠金融背景下,以Lending Club公司公开交易的金融借贷数据为研究对象,采用Python实现对数据的处理及可视化,并使用无监督学习K-Means算法对数据进行聚类分析。分析结果以多类型可视化图形的方式呈现,并结合计算机美学思维,使用户和研究人员能够更加直观的发掘数据潜在价值,有利于对客户的精确细分,助力于金融服务。研究结果表明,基于Python技术的可视化分析能够帮助用户更好的理解庞大复杂的金融数据,同时为决策者提供辅助支持。

In the context of inclusive finance,taking the publicly traded financial lending data of Lending Club as the research object,Python is adopted to process and visualize the data,and unsupervised learning K-Means algorithm is used to cluster and analyze the data.The results are presented in the form of multi-type visual graphics and combined with the thinking of computer aesthetics,which enables users and researchers to explore the potential value of data more intuitively,and facilitates the precise segmentation of customers and the further promotion of financial services.The research results show that visualization analysis based on Python technology can help users better understand the huge and complex financial data,while providing auxiliary support for decision makers.