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Understanding the problem statement
Importing the dataset from AWS
Importing Keras and other libraries and understanding its use
Performing basic EDA and removing unnecessary data
Determine if "log transformation" improves correlation
Creating new features using existing features
Response variables for training and testing sets
Building Artificial Neural Network using keras
Understanding the Parameters of a Neural Network
Understanding parameters that prevent overfitting
Fit the Keras model to the training data
Plotting the Accuracy and Loss with each epoch for visualization
Using Confusion Matrix, Accuracy, Precision ,F1-score, and AUC for evaluating the model
Extracting important features and Correlation visualization
Understanding Positive and Negative correlation
Customer churn refers to the situation when a customer ends their relationship with a company, and its a costly problem. Customers are the fuel that powers a business.
Loss of customers impacts sales. Further, it’s much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn.The good news is that machine learning can help. For many businesses that offer subscription-based services, its critical to both predict customer churn and explain what features relate to customer churn.
Older techniques such as logistic regression can be less accurate than newer techniques such as deep learning, which is why we are going to show you how to model an ANN in R with the keras package.