基于GBDT算法的潜在5G用户预测研究与实现

Study and Implementation of Potential 5G User Prediction Based on GBDT Algorithm

5G用户规模发展是新时代新基建大背景下运营商5G网络建设的终极目标。传统的通过人工方式进行营销发展存在诸多不足,浪费大量人力物力财力。针对这些缺点,基于运营商O域和B域数据引入梯度提升决策树(GBDT)分类算法,通过学习存量5G用户正负样本在历史网络上产生的出账数据和网络数据建立5G用户分类预测模型,做到精准挖掘5G潜在用户,提升市场营销的命中率。研究结果表明,基于GBDT算法的潜在5G用户预测模型能有效预测5G目标用户,提高5G用户转化率,对5G用户发展起到积极推动作用。

The development of 5G user scale is the ultimate goal of operator 5G network construction in the new era of new infrastructure.There are many shortcomings in the traditional marketing development through artificial way,which wastes a lot of human and material resources.In view of these shortcomings,based on the operator O-domain and B-domain data,it introduces gradient boosting decision tree(GBDT)classification algorithm,by learning the stock of 5G user positive and negative samples on the historical network of accounting data and network data,it establishes 5G user classification prediction model,so as to accurately mine 5G potential users,and improve the marketing hit rate.The results show that the potential 5G user prediction model based on GBDT algorithm can effectively predict 5G target users,improve the conversion rate of 5G users,and actively promote the development of 5G users.