Predicting telecom customer churn: An example of implementing in python

The telecom industry is facing fierce competition. Customer retention is becoming a real challenge. Telecom companies do not want their customers to leave them and look for other service providers. Thus addressing customer churn is becoming a problem. This paper determines the customer churn percentage for a given case of transaction data set as a secondary data. The objective of this research is to identify the probability of customer churn using predictive analytics technique using logistic regression model in order to assess the tendency of probability of customer churn. The result of model accuracy got is 0.8. Based on the existing telecom case study data, customer churn percentage is determined as shown in the graph in the main body of the paper and weighting factors of model function are computed using Python programming language and its libraries.