基于机器学习的网络投诉预测分析

减少网络相关的投诉一直是运营商的重点工作之一。目前,网络投诉用户预警方案多以网优工程师经验为主导,准确率和效率都较低。本文通过对历史网络投诉用户数据进行全面深入的分析,基于XGboost算法建立投诉用户特征模型,实现了对网络投诉用户的预测。该方法预测准确率较高,与其他网优系统对接后能够定位用户质差原因,使网络部门能够提前进行网络优化,提升用户满意度。

Prediction and analysis of network complaints based on machine learning

Reducing network related complaints has always been one of the important work of telecom operators. At present, most of the early warning schemes for network complaint users are based on the experience of network optimization engineers, with low accuracy and efficiency. In this paper, through a comprehensive and in-depth analysis of the historical network complaint user data, a complaint user feature model is build based on XGboost algorithm, which can predict network complaint users. This method has a high prediction accuracy, and can locate the reasons for poor quality of users after docking with other network optimization systems, so that the network department can optimize the network in advance and improve user satisfaction.