汽车价格预测中的特征选择:一种综合方法

Sobana Selvaratnam, B. Yogarajah, T. Jeyamugan, N. Ratnarajah
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引用次数: 2

摘要

用于预测的机器学习模型使研究人员能够根据历史数据做出有效的决策。汽车价格预测研究是当今机器学习中最有趣的研究领域。价格模型和价格预测的自变量对汽车消费者和制造商同样重要。汽车咨询公司确定价格与自变量的关系,然后他们可以调整汽车的设计,商业策略和其他因素来实现指定的价格目标。此外,该模型将帮助管理层理解公司的定价模式。机器学习系统预测结果的能力完全依赖于特征的有效选择。本文采用LASSO和逐步选择回归算法相结合的方法确定汽车价格的影响特征。我们使用多元线性回归来使用选择的特征构建模型。从UCI机器学习存储库的汽车数据集的实验结果来看,影响汽车价格的特征是宽度、发动机尺寸、城市英里数、行程、品牌、排量、车门数量、车身样式和驱动轮。训练数据预测价格的准确率为92%,测试数据预测价格的准确率为87%。所提出的方法支持选择最重要的特征,有效地预测汽车价格。这项研究将有助于开发一个模型,该模型使用机器学习技术使用选定的属性来预测汽车的价格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature selection in automobile price prediction: An integrated approach
Machine learning models for predictions enable researchers to make effective decisions based on historical data. Automobile price prediction studies have been a most interesting research area in machine learning nowadays. The independent variables to model the price and the price predictions are equally important for automobile consumers and manufacturers. Automobile consulting companies determine how prices vary in relation to the independent variables and they can then adjust the automobile's design, commercial strategy, and other factors to fulfill specified price targets. Furthermore, the model will assist management in comprehending a company's pricing patterns. The ability of machine learning systems to predict outcomes is entirely dependent on the effective selection of features. In this paper, we determine the influencing features on automobile price using an integrated approach of LASSO and stepwise selection regression algorithms. We use multiple linear regression to build the model using the selected features. From the experimental results using the automobile dataset from the UCI machine learning repository, the influencing features on automobile price are width, engine size, city mpg, stroke, make, aspiration, number of doors, body style, and drive wheels. Training data accuracy for predicting price was found to be 92 %, and testing data accuracy was found to be 87%. The proposed approach supports selecting the most important characteristics of predicting the price of automobiles efficiently and effectively. This research will aid in the development of a model that uses the selected attributes to predict the price of automobiles using machine learning technologies.
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