With the emergence of new competitors in today's globalizing world and the increase in investments in telecommunication services in an environment where change continues, the importance of marketing strategies and the conscious behavior of customers have become an important demand for companies. New regulations and technologies increase competition among mobile operators. Because acquiring a new customer is more expensive than acquiring active customers, companies seek solutions to reduce the churn rate. Therefore, telecommunications companies want to analyze the concept of the customer's desire to change the service provider and take the necessary measures to protect their existing customers. In this study, usage information, usage trends, subscription commitment, subscription age, ARPU (average revenue per user) and billing information, competitor familiarity, outgoing call information, number porting experience, etc. Loss estimation modeling was made taking into account the The data set includes 593 columns and 1826588 lines. Corporate mobile customers were analyzed by dividing into three subgroups as Single Line Mobile Customers, 2-5 Line Mobile Customers, and 6-15 Line Mobile Customers. In order to estimate customer loss, various data mining analyzes were made with machine learning techniques in the SAS application. Four different machine learning methods were used while creating loss prediction models. The machine learning methods used are random forest, logistic regression, decision tree, gradient boosting. The model was developed by modeling with 600 different variables and loss estimation was made. ROC curves and lift chart results for different corporate mobile customer groups were compared and the most suitable models were selected for each group. In this project, it is aimed to prevent loss of revenue for different corporate mobile segment groups by estimating potential customer losses.