A telecommunications firm must forecast client churn (i.e., customers who move from one provider to another). Each clien

[answerhappygod]

A telecommunications firm must forecast client churn (i.e., customers who move from one provider to another). Each client has a history with the business, which includes monthly usage trends, customer care calls, and if the consumer ever opted out of the service. Amazon S3 is used to store all of this data. The business has to identify which consumers are about to churn in order to reclaim their loyalty.

What is the best strategy for meeting these requirements?

A. Use the Amazon Machine Learning service to build the binary classification model based on the dataset stored in Amazon S3. The model will be used regularly to predict churn attribute for existing customers.
B. Use AWS QuickSight to connect it to data stored in Amazon S3 to obtain the necessary business insight. Plot the churn trend graph to extrapolate churn likelihood for existing customers.
C. Use EMR to run the Hive queries to build a profile of a churning customer. Apply a profile to existing customers to determine the likelihood of churn.
D. Use a Redshift cluster to COPY the data from Amazon S3. Create a User Defined Function in Redshift that computes the likelihood of churn.