Customer churn prediction based on a novelty hybrid random forest algorithm

Abstract

With the rapid growth of the economy and consumption, the phenomenon of customer churn becomes more and more rampant. Therefore, predicting whether customers' churn behavior has become a necessary means for enterprises, society, and even the whole country to develop an economic system and create an economic system to ensure that cash flow is not blocked. In fact, the data related to customer prediction have features of huge magnitude and diverse dimensions. Therefore, organizations often need to invest high costs to complete a series of feature projects to capture and analyze feature variables. However, due to the large and complex data, the accuracy of feature engineering is difficult to be guaranteed. Inspired by this phenomenon, this paper proposes a high-quality and good-performance RF-MLP algorithm. Through the training of random forest, the data is filtered and screened and then combined with an artificial neural network to transform the screening results, capturing high-level and nonlinear feature variables and finally achieving stronger model fitting. In this study, the feasibility of the algorithm model is verified by a real and effective data set of customer churn. According to the experimental results, the AUC score of RF-MLP in the test set is 84.9%, which is 9.1% and 21.7% higher than that of the RF and MLP algorithm alone.

Prediction of Telecom Customer Churn Based on MIPCA-XGBoost Method

“…At present, there are a large number of domestic literatures on customer churn, involving a wide range of fields. Zhang Lili [2] and others used decision tree algorithm to predict airline customer churn, and successfully improved the seating rate; Yan Chun [3] and others used BP-Adaboost algorithm to predict the clustered life insurance industry customers, providing a higher prediction accuracy; In the field of e-commerce, Wu Yongchun [4] fused multiple methods to establish a prediction model, which shortened the prediction time. However, the customer data in the telecommunications industry has the characteristics of large quantity and high dimension.…”