Churn prediction with machine learning methodsANGELOVSKA, BLAGICA (Avtor) Marolt, Matija (Mentor) machine learningclassification modelcustomer attritionmarketingChurn rate affects all businesses, from the smallest to the largest companies. It has a big role in the CRM strategy, where it is usually used for analyzing the return on marketing investments. In the gambling industry, it is important to analyze and compare the detected churners, so the marketeers can cleverly use the marketing efforts. Predicting churn rate will not only minimize the outflow of customers, but will also increase their time spent at the casino. In order to be able to determine it, it is essential to understand each individual customer better. In this thesis, we analyze the gathered data on casino players, define a churn definition and use supervised learning methods for predicting customer churn. We focus on gradient boosted trees, more precisely XGBoost (eXtreme Gradient Boosting trees), and explore how their structure and parameters affect the prediction. Through analysis, we proved that by using demographic and visiting information of customers, it is possible to successfully predict the potential churn customers.20192019-09-10 13:10:05Diplomsko delo/naloga109930VisID: 23248sl