Introduction Every bank wants to hold there customers for sustaining their business so the Multinational bank. Below is the customer data of account holders at Multinational Bank and the aim of the data will be predicting the Customer Churn. What is bank Customer Churn Prediction? Businesses tend to focus on voluntary churn (where customers actively choose to leave your service) but involuntary churn (where they leave due to a failure that is outside of their control) Is the most common type, and the most easily addressed (if you can fix it proactively, before the failure). Churn Prediction Model is a predictive model that calculates, on an individual customer basis, the likelihood (or susceptibility) that a customer will stop doing business with the company. It gives you an indication, for each customer at any given time, of how high the risk is that you will lose them in the future.Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones. Bank customer churn prediction is fundamentally a classification problem where we build a classifier to predict whether or not a customer will churn. The target variable of this classification problem is categorical (churn vs. no churn), while the high-level analysis is numerical (the churn rate). Line charts can be used to show churn over time. Bar charts are used to show churn by different categories or can be used as an alternative to line charts to show churn over time. Stacked bar charts allow you to add another dimension to a bar chart, letting you to see in more detail where the churn was coming from
