In order to learn about bank's customers we will make use of one of the Deep Learning techniques,i.e., 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.

1. Data preprocessing - EDA(Exploratory Data Analysis), Feature Scaling
2. Build ANN architecture
3. Training ANN on train set
4. Predicting Test set results

Customer Churn Implementation Code

After calculating the test set results, we got the accuracy of approx. 85% with the help of Hyperparameter Tuning.