| With the development of society and the improvement of people’s living standards,more and more people accept the concept of advanced consumption,which has created a large number of personal credit consumption needs.However,the current personal credit business of commercial bank has the characteristics of small overall scale and low profit contribution rate,which lack sufficient influence on the financial market.At present,commercial banks are also actively expanding users and developing personal credit business.Therefore,using personal credit-related data to mine users through data visualization analysis and control the risk of default is an important issue that needs to be solved urgently.In response to the above problems,through the analysis and research of related technologies,this paper designs and implements a visual analysis system based on personal credit data to help commercial bank business personnel and data analysts to easily complete data analysis and mining work.The research content and main work of this paper is as follows:(1)Designed and implemented personal credit data visualization analysis system included data processing,data visualization analysis and model visualization construction,which can analyze and mine personal credit data.(2)Designed and implemented feature engineering module with feature construction,feature extraction and feature selection functions,which can effectively extract and separate features useful for model learning.(3)Designed and implemented model building module with drag-and-drop data mining process function,which can quickly and efficiently build model and train data.The visual analysis system implemented in this paper mainly includes functional modules such as data source,data processing,data analysis,feature engineering and model construction.By analyzing and designing the specific functions of each functional module,and then taking personal credit-related data as an example to introduce the specific implementation of the system,including data upload,data missing and outlier processing,data visualization and analysis,feature structure,extraction,selection and model visualization construction,to better help commercial bank staff to explore potential users,provide auxiliary decision support,so as to bring profits to the bank. |
