基于深度学习的餐饮客户流失预测  

Catering Customer Churn Prediction Model Based on Deep Neural Network

作　　者：李周辉 Li Zhouhui(Huashang College,Guangzhou University,Guangzhou 510000)

机构地区：[1]广州华商学院,广州510000

出　　处：《现代计算机》2022年第17期47-51,共5页Modern Computer

基　　金：广东财经大学华商学院校级质量工程电子商务特色专业(HS2019ZLGC05)。

摘　　要：客户流失是餐饮活动中的一个重要问题,及时发现流失客户,并为此制定相应的措施,能有效降低餐饮活动的损失。为了研究深度学习在餐饮客户流失中的应用,本文首先分析影响餐饮客户流失的主要因素,筛选出影响餐饮客户流失的有效特征;其次构建基于深度学习的神经网络模型,确定神经网络训练中的各个参数;最后将特征数据输入到模型中做预测,并与Logistic回归和支持向量机两种方法做比较,验证其准确性。实验结果表明,深度学习神经网络模型取得的效果最好。Customer churn is an important problem in catering activities.Timely detection of customer loss and formulation of corresponding measures can effectively reduce the loss of catering activities.In order to study the application of deep learning in the loss of catering customers,the main factors affecting the loss of catering customers were firstly analyzed,and the effective features affecting the loss of catering customers were screened out.Secondly,the neural network model based on deep learning is constructed to determine the parameters of neural network training.Finally,the feature data is input into the model for prediction,and compared with Logistic regression and support vector machine to verify its accuracy.The experimental results show that the deep learning neural network model achieves the best effect.

关 键 词：深度学习 神经网络 客户流失 餐饮 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F719.3[自动化与计算机技术—控制科学与工程] F274[经济管理—产业经济]