The Analysis Of Customer Churn Prediction Based On Machine Learning Method

With the rapid development of the tourism industry,the competition between the tourism market is becoming increasingly fierce.In order to take a leading position in the market share,the tourism service companies continue to expand new business and promote new packages.When users enter a website,some will become the company's users,but the majority will be lost.Therefore,in order to improving the rate of conversion,it is particularly important to retain their users.It is also a reason why we raise the analysis of customer churn in this chapter.By taking advantage of the data collected from consumer behavior in browsing the webpage,we are able to identify the key factors affecting the loss of users.With the research of Machine learning method.we can also forecast the final loss of users.In this paper,four kinds of machine learning algorithms are adopted to establish the prediction model of customer churn the tourism website: logistic regression,SVM,random forest and adaptive boosting algorithm(combined classifier algorithm).Firstly,Preprocessing the dataset,including the processing of missing values and outliers.Then,according to the ratio of 3:1,the dataset is divided into training set and testing set.Detecting the consistence of distribution between train dataset and test dataset,eliminating the variables that have not passed the homogeneity.Finally,we can use four algorithms to establish the model respectively and compare the results of the rules generated by the various models each other.By comparing the results of the four algorithms,it is found that the random forest algorithm has the best predictive effect in this paper.