Deep Learning with Keras in R to Predict Customer Churn

Deep Learning with Keras in R to Predict Customer Churn

In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

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What will you learn

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

Project Description

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.

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Curriculum For This Mini Project

Overview - Understanding Churn
05m
Exploring Dataset
02m
Install Keras Library - 1
04m
Install Keras Library - 2
03m
Install Keras Library - 3
00m
Install Anaconda
01m
Usage of Libraries - 1
03m
Read Dataset
00m
Split Dataset
00m
Observe Data & Chaining Rules
01m
Transformation
03m
Transformations - One Hot Encoding
08m
Create Recipe - 1
01m
Create Recipe - 2
03m
Bake Function
00m
Next Steps
00m
Recipe Recap
04m
Building Artificial Neural Network
00m
Process Explanation
06m
Fit the Model
07m
Plotting - 1
05m
Recap
08m
Improving Model - 1
01m
Improving Model - 2
03m
Plotting - 2
02m
Making Predictions
03m
Confusion Table
00m
Compute Accuracy
00m
Compute AUC (Area Under Curve)
00m
Generate Precision or Recall
01m
F1 Statistics
00m
Setup Lime
00m
Lime Explanation - 1
01m
Explain Model
00m
Feature Importance Visualization
01m
Churn Correlation
00m
Lime Explanation - 2
03m
Interpret Churn Analysis
02m
Improving Model - 3
00m
Re-train Model
02m
Hyper Parameter Tuning - 1
01m
Hyper Parameter Tuning - 2
01m
Kernel Initializer
01m
Optimizer SGD
01m
Optimizer ADAGRAD
02m
Adaptive Subgradient Method
01m
Conclusion
02m