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基于随机森林的用户网购行为数据填充方法研究
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Abstract:
本文针对用户网络购物行为预测问题，研究使用随机森林方法对用户网购行为数据进行填充。首先通过数据分析对数据集中缺失数据的缺失分布、缺失数量以及缺失数据存在依赖性进行分析，结合成对删除、对象删除的方法处理简单缺失数据，再重构数据集，基于随机森林方法对缺失数据进行填补。最后使用不同算法搭建用户网购行为预测模型，对比填补前后的数据集在这些模型下的预测效果，证明了随机森林方法在填补用户网购行为缺失数据中的有效性与通用性。
Aiming at the prediction of user online shopping behavior, this paper studies the filling of user online shopping behavior data by using random forest method. Firstly, through data analysis, the missing distribution, missing quantity and the dependence of missing data in the data set are analyzed. Combined with the methods of paired deletion and object deletion, the simple missing data are processed, and then the data set is reconstructed to fill the missing data based on the random forest method. Finally, different algorithms are used to build user online shopping behavior prediction models, and the prediction effects of the data sets before and after filling are compared under these models, which proves the effectiveness and universality of the random forest method in filling the missing data of user online shopping behavior.
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