Afundamental truth in the business world is that competitors are always looking to steal your customers, and many customers are on the lookout for a better deal. customer attrition rates range from seven percent annually in some industries with high exit barriers, like banking and insurance, to nearly 40 percent in the mobile phone industry. slowing the customer "churn" rate by as little as one percent can add millions of dollars to any sizable company’s bottom line. as it’s a great deal more expensive to acquire customers than to retain them, an effective customer retention strategy is crucial to a company’s success. mobile phone service is viewed as a commodity by customers all over the world. as is often the case for a new market, cell phone providers have concentrated on customer acquisition at the expense of service quality and retention. this has created a buyer’s market where customers are motivated to get the best deal possible during the sign-up or contract phase of their relationship with a carrier. the majority of customers are open to changing carriers to reduce monthly fees, increase their number of minutes, or to receive a better phone. many mobile phone providers must now reinvent themselves in order to shift the focus of their corporate culture to providing an outstanding experience and retaining customers. therefore, identifying customers who are about to churn (cancel their service) is potentially worth millions of dollars if the company can proactively address the reason why that customer is considering cancellation and retain the customer. the file cellphone_1 contains customer data to be used to classify a customer as a churner or not. a. do a standard partitioning of the data into training and validation sets (60% and 40% respectively). (no need for a test set in this assignment)b. using churn as the output variable (and all other variables as input variables), classify the data using k-nearest-neighbors (knn) algorithm. fit the model to search for the best solution using # neighbors (k) from 1 through 10 neighbors. rescale data to standardization type to adjust the different magnitudes of the variables.