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