Research on Personal Credit Rating Prediction Model Based on Machine Learning for Unbalanced Data
This article aims to study a personal credit score prediction model based on machine learning under imbalanced data.Firstly,the basic concept of personal credit scoring was intro-duced,as well as the application of imbalanced data and its processing methods,as well as ma-chine learning algorithms in credit scoring.Then,through data preprocessing,including data sources and characteristics,data cleaning and organization,data imbalance analysis,data augmen-tation methods and effectiveness evaluation,a foundation is provided for subsequent model con-struction.Finally,use actual datasets for model training and testing,and evaluate the performance of the model.The experimental results show that the personal credit score prediction model based on machine learning under imbalanced data can effectively predict personal credit risk,which is of great significance for risk management and credit decision-making of financial insti-tutions.
personal credit scoreUnbalanced dataMachine learningData preprocessingmodel research
万方数据
维普
