Analysis And Prediction Of Churn Customers using Machine Learning

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Analysis And Prediction Of Churn Customers using Machine Learning

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Churn Analysis is one of the world wide used analysis on Subscription Oriented Industries to analyze customer behaviors to predict the customers which are about to leave the service agreement from a company. It is based on Machine Learning methods and algorithms and become so important for companies in today’s commercial conditions as gaining a new customer’s cost is more than retaining the existing ones. The paper reviews the relevant studies on Customer Churn Analysis on Telecommunication Industry in literature to present general information to readers about the frequently used data mining methods used, results and performance of the methods and shedding a light to further studies. To keep the review up to date, studies published in last five years and mainly last two years have been included.


We review the existing works on churn prediction in three different perspectives: datasets, methods, and metrics for banking sectors. Firstly, we present the details about the availability of public datasets and what kinds of customer details are available in each dataset for predicting customer churn. Secondly, we compare and contrast the various predictive modeling methods that have been used in the literature for predicting the churners using different categories of customer records, and then quantitatively compare their performances. Finally, we summarize what kinds of performance metrics have been used to evaluate the existing churn prediction methods. Analyzing all these three perspectives is very crucial for developing a more efficient churn prediction system for telecom industries.


  • We predicted only banking sector customers’ dataset.
  • In today’s technological conditions, new data are being produced by different sources in many sectors.
  • However, it is not possible to extract the useful information hidden in these data sets, unless they are processed properly.
  • In order to find out this hidden information, various analyses should be performed using data mining, which consists of numerous methods.


Studies revealed that gaining new customers is 5 to 10 times costlier than keeping existing customers happy and loyal in today’s competitive conditions, and that an average company loses 10 to 30 percent of customers annually. Many companies, being aware of this fact, are engaged in satisfying and retaining the customers. Especially in the subscription oriented industries, such as telecommunications, banking, insurance, and in the fields of customer relationship management, etc., companies working with numerous customers, the revenues of the companies are provided by the payments made by these customers periodically. It is very important to be able to keep customers satisfied in order to be able to sustain this revenue with the least expenditure cost.


  • Reviewing the relevant studies about churn analysis on telecommunications industry presented in the last five years, particularly in the last two years, and introducing these up-to-date studies in the literature
  • Determining the data mining methods frequently used in churn implementations
  • Shedding a light on methods that can be used in further studies.





  • OS – Windows 7,8 or 10 (32 or 64 bit)
  • RAM – 4GB


  • Python IDLE
  • Anaconda – Jupyter Notebook


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