{"title":"运用logistic回归预测旅游保险保单理赔","authors":"Dadang Amir Hamzah","doi":"10.31098/quant.613","DOIUrl":null,"url":null,"abstract":"This paper analyzes the characteristics that influence the travel insurance claim based on existing data records. Using logistic regression, the dependent variable is the feature that determines whether there is a claim or no claim. On the other hand, the independent variables are analyzed using exploratory data analysis to identify which characteristic has the highest correlation with the dependent variable. Based on selected features, the logistic regression model is created and used to generate the prediction claim data. The predicted data gives an excellent approximation to the actual data.","PeriodicalId":186902,"journal":{"name":"Applied Quantitative Analysis","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting travel insurance policy claim using logistic regression\",\"authors\":\"Dadang Amir Hamzah\",\"doi\":\"10.31098/quant.613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyzes the characteristics that influence the travel insurance claim based on existing data records. Using logistic regression, the dependent variable is the feature that determines whether there is a claim or no claim. On the other hand, the independent variables are analyzed using exploratory data analysis to identify which characteristic has the highest correlation with the dependent variable. Based on selected features, the logistic regression model is created and used to generate the prediction claim data. The predicted data gives an excellent approximation to the actual data.\",\"PeriodicalId\":186902,\"journal\":{\"name\":\"Applied Quantitative Analysis\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Quantitative Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31098/quant.613\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Quantitative Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31098/quant.613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting travel insurance policy claim using logistic regression
This paper analyzes the characteristics that influence the travel insurance claim based on existing data records. Using logistic regression, the dependent variable is the feature that determines whether there is a claim or no claim. On the other hand, the independent variables are analyzed using exploratory data analysis to identify which characteristic has the highest correlation with the dependent variable. Based on selected features, the logistic regression model is created and used to generate the prediction claim data. The predicted data gives an excellent approximation to the actual data.