基于并行C4.5的铁路零散白货客户流失预测研究

为了提高铁路零散白货客户流失预测的准确性和高效性，根据铁路零散白货客户的流失特征，提出了基于CDL模型的客户流失识别方法，在此基础上，针对数据量大的问题，提出了基于Hadoop并行框架的C4.5决策树客户流失预测模型。通过仿真实验，证明该模型具有较好的准确性和预测能力，并且随着样本数量的增加，Hadoop并行框架的效率得到了明显的提升，且不影响客户流失预测模型的准确性和预测能力。

Research on railway scattered freight customer churn prediction based on parallel C4.5 decision tree algorithm

In order to improve the accuracy and efficiency of customer churn prediction of railway scattered freight, according to the loss characteristics of railway scattered freight customers, proposed a customer churn identification method based on CDL model. On this basis, facing the problem of big data, proposed a C4.5 decision tree customer churn prediction model based on Hadoop parallel framework. Simulation results show that the model has good accuracy and predictive ability, and as the number of samples increases, the efficiency of Hadoop parallel framework is obviously improved, and the accuracy and prediction ability of churn prediction model are not affected.