基于CART和自适应Boosting算法的移动通信企业客户流失预测模型

客户流失问题一直以来都受到企业的重视，如何有效预测流失客户是一个重要课题。本文通过对某通信企业原始数据进行严格的数据预处理，以及利用直方图检验和卡方检验相结合的方法对模型变量进行筛选，同时采用抽样的方法选取出模型的训练样本和测试样本，并利用分类回归树算法和自适应Boosting算法生成相应的强分类器模型，仿真实验结果表明本文使用的模型在预测该通信企业的离网客户中具有较高的准确性，从模型的ROC曲线可知，该模型是一个比较理想的分类模型。另外，本文通过与其他两个模型的预测结果进行比较发现本文的集成模型具有更好的预测性能。

Customer Churn Prediction in Mobile Communication Enterprises based on CART and Boosting Algorithm

Customer churn problems have been taking seriously by Enterprises, and how to predict churning customers effectively has been becoming an important subject. Firstly the original data of a mobile communication enterprise is preprocessed strictly, and the histogram test and the chi-square test are employed for choosing variables for the prediction model. Then a sampling method is applied to extract data for training and testing, and a strong classifier model based on Classification and Regression Tree and adaptive boosting algorithm is constructed by using training samples. At last, a simulation experiment is adopted and the results of the experiment show that the integrated model used in this paper had high prediction precision. The ROC curve presented in the paper also illustrates the model is an ideal classification model. Meanwhile, the model has been proved to have better prediction performance by comparison with the other two models.