This project is developed to predict the Customer Churning out of from a Telecom Operator and factors impacting this process. The company uses data analytics to detect the customer who are likelt to leave network operator. This way, they will be able to analyse the customers and their reason to for leaving the network operator.
- Which variables are significant in predicting the customer churn rate, and
- How well those variables describe the customer churn rate.
Also, determine the optimal value of lambda for ridge and lasso regression.
You are required to model the percentage rate of customer with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the customers are getting impacted.