Customer Churn is a business metric that refers to when a customer leaves or stops doing business with a company. It is in every business's interest to keep this metric low because it is more expensive to acquire new customers than to retain or keep an existing customer.
This is where Customer Churn Prediction comes into play; Churn prediction means detecting which customers are likely to leave a service or cancel a subscription to a service. After identifying customers who are at risk of canceling, marketing strategies can be carried out on individual customers, to maximize the likelihood of retaining them.
In essence, this project focuses on utilizing machine learning to predict customer churn. The prediction task uses a Linear regression learning algorithm to build a model that learns the dataset and can predict new observations.
Using K_fold cross-validation, the model records an accuracy of 100% on both the training and validation datasets.