The Model of Customer Churn PredictionOn Machine Learning–A Case of An Asset ManagementCompany

• Main Authors: FANG,LUNG-WEI, 方龍偉
• Other Authors: LIN, WEN-SHIU
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/5h8ezx

碩士 === 輔仁大學 === 資訊管理學系碩士在職專班 === 105 === In the highly competitive wealth management market, the investment company to develop new customers high cost, the fight to the customers and high turnover rate. Therefore, if the investment company to focus on maintaining and obtaining the trust of existing customers, understand customer needs and provide customers with the best products and rewards, in order to avoid the loss of existing customers, its costs, economy and profitability of enterprises benefits willcontribute to higher than other industries. This has sparked research onthe telecommunications company of the customer churn prediction modelfor high level of interest. This study is divided into two topics, one is to define the RFM model of the object in line with the indicators and explore whether the RFM model indicators help to enhance the ability of model prediction The second is comparative gene expression programming method, C4.5 Decision Tree, Random Forest, Support Vector Machine the Correct Rate of Customer Loss Prediction Model and Its Advantages and Disadvantages of Vector Machine Outputand. In experimentalconclusion,the Model 2 is based on the model 1 variables and added RFM variables,the four algorithms of the Accuracy, Precision, FPR and F-Measure are better than the modelone, showing that RFM is one of the factors affecting customer churn.In addition,this study also summarizes the characteristics of each algorithm, if the efficiency of the algorithm to evaluate, you can choose C4.5 Decision tree modeling, if the effect of the algorithm to evaluate, choose Random Forest modeling, The explanatory power of the model can be evaluated by C4.5 or Gene Expression Programming.