基于Bootstrap和随机森林的二手车价格预测算法的研究与设计
首发时间:2021-04-14
摘要:二手车在共享经济的推动下得到了飞速发展,尽管如此,二手车市场毕竟是新时代下的新兴产物,其市场成熟度仍有很大提升空间。随着二手车的发展,二手车价格的预测成为该领域的研究重点,这也是保障消费者和商家利益的重要手段。本论文提出了一种基于Bootstrap和随机森林算法的二手车价格预测算法Bootstrap-RF,首先对实验中所用到的二手车数据进行预处理,其次利用Bootstrap进行重采样,再次使用随机森林算法进行二手车价格预测模型的训练和优化,最后与其他机器学习算法的实验效果进行了对比分析,实验证明Bootstrap-RF算法提高了二手车价格预测的准确率,为消费者在购买二手车的时候提供了价格参考和理论支撑,对二手车市场的发展意义重大。
关键词: 计算机应用 二手车价格 机器学习 Bootstrap 随机森林
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Research and Design of Second-hand Car Price Prediction Algorithm Based on Bootstrap and Random Forest
Abstract:Drived by the sharing economy, the second-hand car market has developed rapidly. Despite this, the second-hand car market is an emerging product in the new era, and there is still a lot of room for improvement in its market maturity.With the development of second-hand car, the prediction of second-hand car price has become the focus of research in this field, which is also an important means to protect the interests of consumers and merchants.This paper puts forward a used-car prices based on the Bootstrap and random forest algorithm prediction algorithm the Bootstrap-RF, first used in the experiment were used data preprocessing, secondly using the Bootstrap resampling, again using random forest algorithm used car price forecasting model training and optimization, and the experimental results with other machine learning algorithms through the contrast analysis of experiments prove that the Bootstrap - RF algorithm improves the accuracy of used-car prices forecast, offers when consumer is buying a used car price reference and theoretical support,It is of great significance to the development of the second-hand car market.
Keywords: Computer application Bootstrap Random Forest Machine learning Used car price
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