Client turnover in the banking industry has grown according to the report. Churn can be classified into a variety of types. It s common knowledge that the cost of acquiring a new client is significantly greater than that of the expense of keeping an existing one. The objective is to find the most accurate machine learning-based churn prediction systems feasible. The entire dataset will be analysed using the supervised machine learning approach (SMLT) to gather a variety of data points including variable identification missing value treatments data validation data cleaning and data visualisation Identify the confusion matrix and categorise data from the supplied credit card dataset as well as compare and assess multiple machine learning techniques performance with an evaluation classification report from the given credit card dataset.