Dimensionality reduction visualization analysis of financial data based on semantic feature group

With the continuous development of data science and financial technology, financial data visualization methods have become an essential key technology in the field of financial data analysis today. From the technical point of view, the mainstream visualization analysis takes the fusion of large screen and multiple views, and the nature of its visualization effect is more focused on the enumeration display, without fully analyzing the data characteristics from the essence. The single view visualization analysis technology is difficult to get clear and effective visualization display through correlation analysis, dimensionality reduction algorithms and principal component analysis. From the application point of view, credit card customer data, as an important part of financial data, has positive practical significance in customer profiling, product recommendation and risk prediction, and the targeted improvement research of its visualization method has an important role. The semantic feature group method combines the domain knowledge and data distribution characteristics of credit card customer churn data, composes and analyzes the semantic feature groups, and obtains explicit visualization and analysis results by combining the understanding of the actual problem and the numerical characteristics of the data itself. The accuracy and efficiency of the data representation based on the semantic feature group method are verified by comparing the data dimensionality reduction visualization methods such as multi-view fusion method, T-distribution random neighborhood embedding and principal component analysis in the experiment.