Churn Analysis in Telecommunication Using Logistic Regression

Since the beginning of data mining the discovery of knowledge from the Databases has been carried out to solve various problems and has helped the business come up with practical solutions. Large companies are behind improving revenue due to the increase loss in customers.

The process where one customer leaves one company and joins another is called as churn. This paper will be discussing how to predict the customers that might churn, R package is being used to do the prediction. R package helps represent large dataset churn in the form of graphs which will help to depict the outcome in the form of various data visualizations. Churn is a very important area in which the telecom domain can make or lose their customers and hence the business/industry spends a lot of time doing predictions, which in turn helps to make the necessary business conclusions. Churn can be avoided by studying the past history of the customers. Logistic Regression is been used to make necessary analysis. To proceed with logistic regression we must first eliminate the outliers that are present, this has be achieved by cleaning the data (for redundancy, false data etc) and the resultant has been populated into a prediction excel using which the analysis has been performed.