基于k -最近邻模型的二手车价格预测

K. Samruddhi, R. A. Kumar
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引用次数: 21

摘要

预测二手车的价格是一个重要而有趣的分析领域。随着二手车市场需求的增加,买卖双方的业务都有所增加。由于汽车的价格取决于许多重要因素,因此需要对该领域的专业知识进行可靠和准确的预测。本文提出了一种基于KNN (K最近邻)回归算法的监督机器学习模型,用于二手车价格分析。我们用从Kaggle网站收集的二手车数据来训练我们的模型。通过本实验,采用不同的训练比和测试比对数据进行检验。结果表明,该模型的拟合精度在85%左右,是最优模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Used Car Price Prediction using K-Nearest Neighbor Based Model
: Predicting the price of used cars is one of the significant and interesting areas of analysis. As an increased demand in the second-hand car market, the business for both buyers and sellers has increased. For reliable and accurate prediction it requires expert knowledge about the field because of the price of the cars dependent on many important factors. This paper proposed a supervised machine learning model using KNN (K Nearest Neighbor) regression algorithm to analyze the price of used cars. We trained our model with data of used cars which is collected from the Kaggle website. Through this experiment, the data was examined with different trained and test ratios. As a result, the accuracy of the proposed model is around 85% and is fitted as the optimized model.
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