{"title":"基于机器学习方法,根据生活水平分析房价预测","authors":"K. Mahboob, Nida Khalil, Saniah Rehan","doi":"10.35453/nedjr-ascn-2022-0001r3","DOIUrl":null,"url":null,"abstract":"There is a lack of reliable economical methods for forecasting house prices for those who wish to buy a house according to their living standards. This paper presents details of predictive analytics for house pricing in three different towns of Karachi, Pakistan according to different living standards based on machine learning (ML) methods. The purpose of this study is to determine which data set features contribute greatly to the accuracy of the predictions when experimenting with selected predictive techniques. The house price value has been analysed using five different ML methods. A model selection has been made by comparing the accuracy of the techniques based on some performance metrics and the best technique was used to predict the house price value.","PeriodicalId":259216,"journal":{"name":"NED University Journal of Research","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ANALYSING HOUSE PRICE PREDICTIONS ACCORDING TO LIVING STANDARDS BASED ON MACHINE LEARNING METHODS\",\"authors\":\"K. Mahboob, Nida Khalil, Saniah Rehan\",\"doi\":\"10.35453/nedjr-ascn-2022-0001r3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is a lack of reliable economical methods for forecasting house prices for those who wish to buy a house according to their living standards. This paper presents details of predictive analytics for house pricing in three different towns of Karachi, Pakistan according to different living standards based on machine learning (ML) methods. The purpose of this study is to determine which data set features contribute greatly to the accuracy of the predictions when experimenting with selected predictive techniques. The house price value has been analysed using five different ML methods. A model selection has been made by comparing the accuracy of the techniques based on some performance metrics and the best technique was used to predict the house price value.\",\"PeriodicalId\":259216,\"journal\":{\"name\":\"NED University Journal of Research\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NED University Journal of Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35453/nedjr-ascn-2022-0001r3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NED University Journal of Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35453/nedjr-ascn-2022-0001r3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ANALYSING HOUSE PRICE PREDICTIONS ACCORDING TO LIVING STANDARDS BASED ON MACHINE LEARNING METHODS
There is a lack of reliable economical methods for forecasting house prices for those who wish to buy a house according to their living standards. This paper presents details of predictive analytics for house pricing in three different towns of Karachi, Pakistan according to different living standards based on machine learning (ML) methods. The purpose of this study is to determine which data set features contribute greatly to the accuracy of the predictions when experimenting with selected predictive techniques. The house price value has been analysed using five different ML methods. A model selection has been made by comparing the accuracy of the techniques based on some performance metrics and the best technique was used to predict the house price value.