Binary Classification Model for Santander Customer Satisfaction Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Santander Customer Satisfaction dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: Santander Bank sponsored a Kaggle competition to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer’s happiness before it’s too late. In this competition, Santander has provided hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience. The exercise evaluates the submissions on the area under the ROC curve (AUC) between the predicted probability and the observed target.

In this Take1 iteration, we will construct several Multilayer Perceptron (MLP) models with two hidden layers. We will also observe the best result that we can obtain using the two-layer model. Lastly, we will apply the MLP model to Kaggle’s test dataset and submit a list of predictions to Kaggle for evaluation.

ANALYSIS: From this Take1 iteration, all two-layer models achieved a ROC-AUC performance of between 79.9% and 81.1% after 25 epochs using the validation dataset. The 64/32-node model appeared to have the highest ROC-AUC of 81.142% with low variance. Lastly, when we applied the two-layer neural network model to the test dataset from Kaggle, we obtained a ROC-AUC score of 80.460%. We captured additional performance measurements using different model configurations.

  • Two-Layer 32/16-Node MLP Model – ROC-AUC: 80.9190%
  • Two-Layer 64/32-Node MLP Model – ROC-AUC: 81.1427%
  • Two-Layer 128/64-Node MLP Model – ROC-AUC: 80.0112%
  • Two-Layer 160/96-Node MLP Model – ROC-AUC: 79.9403%
  • Two-Layer 256/128-Node MLP Model – ROC-AUC: 80.6845%
  • Two-Layer 320/192-Node MLP Model – ROC-AUC: 80.5817%
  • Two-Layer 512/256-Node MLP Model – ROC-AUC: 80.8535%

CONCLUSION: For this iteration, the two-layer model with 64/32 nodes appeared to have yielded the best result. For this dataset, we should consider experimenting with more MLP models with different configurations.

Dataset Used: Santander Customer Satisfaction Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.