Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

Sammanfattning: With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector Machine, Boosted Decision Tree and Artificial Neural Network are used and compared as learning algorithms. The experiment shows the model trained by Boosted Decision Tree delivers the best result in this project. Moreover, the discussion about the findings in the project are presented.