Telecom Customer Churn Prediction Based On Grey Wolf Optimization Algorithm And SV

With the rapid development of science and technology,market competition intensified,the communications industry is no exception,in order to meet the market need,each service provider to continuously introduce more high quality service,customers also can choose satisfactory supplier,also the problem of loss of telecom customer behavior prediction becomes the hot topic in current research.In this paper,part of foreign telecom customer data in 2022 was obtained from Kaggle competition data network.After data preprocessing,4,835 samples were finally retained,and the predictive analysis was carried out through descriptive statistical analysis,machine learning model and intelligent optimization algorithm.The thesis work is described as follows:1.Telecom customer data preprocessing.Firstly,the missing and invalid values in the data set are processed,and then the discrete features in the data are mapped and the range standardized data is adjusted to the 0-1 interval.Descriptive statistical analysis was made on the characteristics of customers,such as gender,age,package type,Internet connection type,bill payment method,contract type and service months.2.Propose GMGWO algorithm and construct support vector machine model.Grey wolf optimizer(GWO)has the disadvantages of low precision,slow convergence and easy to fall into local optimal when it is used for feature selection and parameter optimization of support vector machine(SVM).In Chapter 3,according to the GWO algorithm theory,a nonlinear transition parameter based on cosine function was designed to replace the original linear transition parameter of GWO.Meanwhile,Gaussian mutation(GM)was introduced to enhance the population diversity,and an improved GWO algorithm(named as GMGWO)was proposed.The GMGWO algorithm is used for combination optimization of feature selection and SVM parameter and the GMGWO-SVM classification model is formulated.Ten UCI data sets were selected for the experiment.The results show that the classification accuracy of GMGGO-SVM on 10 data sets is improved,while the average redundancy features of 77% are removed.3.Empirical analysis of GMGWO-SVM algorithm.Applying the GMGGO-SVM model to the empirical analysis of telecom customer churn behavior prediction,the GMGGO-SVM algorithm can remove 62% of the redundancy features on average,and improve the classification accuracy of the SVM model by 4.2%.The prediction results of the GMGGWO-SVM algorithm were analyzed through the accuracy rate,recall rate,AUC value and other indicators.The analysis results show that the accuracy of the GMGGWO-SVM algorithm in predicting positive cases(lost customers)is high,and the classification prediction effect is good in the telecom customer data,that is,the GMGGWO-SVM algorithm is suitable for the prediction of telecom customer loss behavior.In addition,according to the selection times of features in ten runs,a new and more representative feature subset is proposed.