Deep Learning Case Studies with Tensorflow and Keras for Beginners-Advanced: ANN, CNN, RNN, Self-Organizing Maps, Boltzmann Machines, Stacked Autoencoders
File names: Artificial_Neural_Network_Case_Study.py, , ANN_Case_Study_Sample_Output_1.png, ANN_Case_Study_Sample_Output_2.png
In this Case Study we predict the churning rate of the customers from the bank. In order to learn about bank’s customers we will make use of one pf the Deep Learning techniques, the Artificial Neural Networks (ANN).
Moreover, we will use popular Python libraries such as Tensorflow, Keras and Machine Learning techniques such as Adam Optimizer to train the ANN model and predict the churn rates. The data consists of 10K randomly selected customers. We will use customer’s characteristics to determine his/her probability of leaving the bank. The data contains 13 variables characterizing 10L bank customers. These variables are CustomerId Surname, CreditScore, Geography Gender, Age, Tenure Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary and Exited.