文章基本信息

标题：ANALYSIS ON PRE-OWNED CARS USING ENSEMBLE MACHINE LEARNING TECHNIQUES
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作者：A.Uhapriya ; A.ChittiBabu ; A.Rajeswari 等
期刊名称：International Journal of Early Childhood Special Education
电子版ISSN：1308-5581
出版年度：2022
卷号：14
期号：2
页码：6056-6060
DOI：10.9756/INT-JECSE/V14I2.688
语种：English
出版社：International Journal of Early Childhood Special Education
摘要：The global market for pre-owned cars, or so-called used cars, is enormous. The buyer of a used car should be able to determine whether or not the price tag placed on the vehicle is accurate before making a purchase. Before purchasing a pre-owned vehicle, a number of factors, including mileage, year, model, make, run, and more, must be taken into account. There should be a level playing field for both the vendor and the customer. Predicting a reasonable price for a used car is the subject of this study, which details a system that has been put into practise. The technology does a good job of predicting used automobile prices in the Mumbai area. Random Forest and eXtreme Gradient Boost are two machine learning approaches used to construct models that can estimate the price of secondhand cars. To find the best method, the techniques are compared. eXtreme Boost outperformed the random forest approach, but they were both comparable in terms of speed. Square Root of Random forest had an error rate of 3.44, while eXtreme Boost had a rate of 0.53.
关键词：The global market for pre-owned cars;or so-called used cars;is enormous