Dataset - large sample of the bank's customers.
To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.
Our goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Also, rank all the customers of the bank, based on their probability of leaving.
Results - 86% Accuracy achieved. Comments added in the code about the process.