A Random Forest Churn Prediction Model: An Study of Machine Learning Techniques for Churn Prediction and Factor Identification in the Telecom Sector

Abstract

This paper presents an examination of machine learning techniques for churn prediction and factor identification in the telecom sector. In particular, the authors propose a random forest churn prediction model. This model utilizes a combination of random forests and data mining techniques to identify customer churn risk factors. The authors then evaluate the performance of the model on a dataset from a large telecom provider. The results show that the model is able to accurately predict customer churn, with an average accuracy of 89%. Furthermore, the model is able to identify the main risk factors for churn, including customer age, gender, number of services, and average monthly spend. This study provides valuable insights into machine learning techniques for churn prediction and demonstrates that random forests can be an effective tool for predicting customer churn.