In this project, the objective is to analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.The dataset contains customer-level information for a span of four consecutive months - June, July, August and September.
The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months