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Bank Customer Churn Prediction

Predict customer churn for a bank using a) ANNs b) Tree-Based Methods (AdaBoost, Random Forest, XGBoost, Decision Tree, Gradient Boosting)

Major concepts dealt with:

  • Data preprocessing (categorical/continuous features) and EDA
  • Need for stratified sampling while creating training and test sets
  • Understanding why scaling and class imbalance corrections should occur after train_test_split
  • Class imbalance techniques used (oversampling): SMOTE, ADASYN
  • Feature scaling (min-max), and why we need it
  • Checking train and test accuracy with and without handling class imbalance
  • Sequential ANN, with experimentation of hyperparameters including choice of optimization techniques
  • Comparison between tree-based methods and ANN to identify which method is better.



Approaches

  1. Artificial Neural Network (Keras Sequential NN)
  2. Tree-based methods: AdaBoost, Random Forest, XGBoost, Decision Tree, Gradient Boosting (sklearn)

Results

Accuracy

  • Accuracy for imbalanced dataset:
    • Random Forest and Gradient Boosting (87% for both) were the best. NN accuracy was (86%).
  • Accuracy for balanced dataset after SMOTE:
    • Random Forest and XGBoost (85% for both) were the best. NN accuracy was (82%).

In terms of accuracy, Random Forest was the best for both balanced and imbalanced datasets. Notably, there is a slight decrease in accuracy after oversampling, for both tree-based and NN approaches.

Precision

  • Precision for imbalanced dataset:
    • Tree-based: Not Exited-0.88 (avg) and Exited-0.71 (avg)
    • NN: Not Exited-0.88 and Exited-0.71
  • Precision for balanced dataset after SMOTE:
    • Tree-based: Not Exited-0.9 (avg) and Exited-0.57 (avg)
    • NN: Not Exited-0.92 and Exited-0.54

Precision for both balanced and imbalanced datasets is almost similar in tree-based (Random Forest/XGBoost/Gradient Boosting) and NN approaches.

Conclusion

A tree-based approach like random forest can be used in this classification problem without NN, since NN is not leading to significant increase in either precision or accuracy.