Deep-learning-Churn-Modelling-Problem
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
Numpy, Pandas, Keras(TensorFlow as backend)
Solving a data analytics challenge for a bank. Given a dataset with a 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.
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). Besides, asked to rank all the customers of the bank, based on their probability of leaving. To do that, need to use the right Deep Learning model, one that is based on a probabilistic approach.
On succeeding in this project, will create significant added value to the bank. By applying Deep Learning model the bank may significantly reduce customer churn.