Under the influence of big data and online marketing,retail enterprises also actively adopt big data intelligent mar-keting scheme.In order to improve the accuracy of retail enterprises in the prediction of consumer behavior,so as to improve the sales of enterprises and obtain greater profits,it extracts features from consumption data,member data and commodity data through the method of information gain,constructs basic feature groups,and combines features to mine potential information.Genetic algo-rithm is used to optimize the parameters of random forest,and construct a stochastic forest consumption behavior prediction model based on parameter optimization.The experimental data comes from 37 day consumption records of an offline chain drugstore.The experimental model is compared with the original random forest model,decision tree model,SVM model and XGBoost model,the experimental results show that the accuracy and AUC value of the model are higher than the other four models.
维普
万方数据