Customer churn prediction with popular machine learning algorithms

In today's increasingly competitive environment, it is necessary to follow the needs, demands, and expectations of customers closely for the enterprises and to respond in the most appropriate and fastest way. It aims to gain customer loyalty by developing mutual relations with customers and thus to provide long-term benefit to the enterprise. Today, the cost of earning new customers is much more than the cost of keeping existing customers. Providing promotions, discounts, gifts, or benefits to the customers who are anticipated to churn may hinder the churn customer and thus make more profit in the long term. However, if the wrong prediction is made, this causes unnecessary promotions or gifts to the customer. So for the company, this means unnecessary costs. Therefore, it is important for companies to correctly estimate the churn of customers. With the help of technology, enterprises can analyze the data they collect from different sources by using various data mining methods and obtain more valid information about the customers, and thus develop more effective communication with customers and ensure their continuity. The aim of this study is to analyze the results of customer churn prediction using various data mining techniques and classification algorithms of machine learning. The data analyzed were obtained from a telecommunication company. In the data set, there were 7166 customer records including the data about customer churn. This study also aims to estimate customers' churn with the highest rate. With the train test split, the data set was divided into 70% - 30% training and test data set. Scale and log transformations are performed on data. The performance of the models obtained by classification algorithms was examined.