Bank Customers Churn Classification with Deep Learning

Classifying bank customers based on their previous bank activities, that they will exit/leave the bank or not.

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.

Aim

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.

Building Artificial Neural Network (ANN)

Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. ANNs have three layers that are interconnected. The first layer consists of input neurons. Those neurons send data on to the second layer, which in turn sends the output neurons to the third layer. ANNs are considered non-linear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found.

Results - 86% Accuracy achieved.

Kaggle notebook: https://www.kaggle.com/lykin22/bank-customer-s-churn-classification-ann-dl