Unusal churn rate detection using Artificial Neural Networks Introduction: You may be familiar with deep learning, a type of machine learning that uses a multilayer design known as neural networks, which is where the term "neural network" comes from. We develop a network of artificial neurons that is similar to brain neurons in the form of a computer network. The artificial neural network is made up of artificial neurons, which are collection nodes that model the neurons in a biological brain. Neurons are associated with a huge number of other neurons in this system, and they are organized into layers. The full neural network is made up of numerous levels, which are referred to as layers. Simply, customer churn is the rate at which customers stop doing business with a company. Merely, churn prediction is the process of predicting whether or not a company's customers will discontinue doing business with it. To put it another way, if a customer purchases a subscription to a service, we must evaluate the likelihood that the customer would leave or cancel the subscription. It's a crucial prediction for many firms because getting new customers is sometimes more expensive than keeping old ones. Customer churn is a metric that tracks how and why customers leave a company. There are numerous methods for calculating client turnover. One method is to divide the number of consumers who leave a business in a certain time interval by the total number of customers present at the start of the period.

We know that customer churn is crucial in business challenges, and that the capacity to anticipate that a certain client is at a high risk of churning while there is still time to intervene is critical. To better understand, consider the following scenario: you have purchased a premium subscription to a company product, and now you believe it is time to cancel your subscription. You will contact the company, and the company will attempt to offer you some additional functionalities in exchange for not canceling your subscription. This is because every industry will suffer if a certain percentage of customers do not use their product. A data science team is present to anticipate customer attrition based on various features in this type of situation. It's time to start working on the artificial neural network that will predict client attrition. First and foremost, we'll need a dataset on which to execute our strategy.

Working: A churn prediction model works in 5 steps: i) Problem identification; ii) Dataset selection; iii) Investigation of data set; iv) Classification; v) Clustering