Abstract:
Over the last two decades, we have seen mobile telecommunication become the dominant communication medium. In many countries, especially developed ones, the market has reached a degree of saturation where each new customer must be won over from the competitors. Since the cost of winning a new customer is far greater than the cost of preserving an existing one, mobile carriers have been shifting considerable attention from customer acquisition to customer retention. As a result, churn prediction has emerged as a crucial mobile Business Intelligence (BI) application that aims at identifying customers who are about to transfer their business to a competitor (i.e., to churn).
This aim of this project was to develop a churn prediction model using commonly used Machine Learning Algorithms. In order to analyze the trends and generate results, anonymous data pertinent to Telecom Industry was collected from a local telecom company. This data was later cleansed to remove all anomalies and missing entries. Well known classifier algorithms were used especially C5.0, Classification & Regression Tree and Discriminant Analysis. A separate model for each classifier was generated, trained and tested on the dataset in order to find patterns which helped in pointing out and predicting possible churners.
