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Conclusion

Summary​

In this tutorial, you explored the process of building an NLP model for predicting customer disputes using H2O Driverless AI. From uploading the dataset to configuring an NLP experiment, analyzing results, and interpreting key variables, you learned how automated machine learning can effectively tackle a critical challenge in the banking sector.

By following these steps,

  • You learned how to upload the Consumer Complaint Resolution Dataset to H2O Driverless AI.
  • You configured an NLP experiment, leveraging automated feature engineering, model selection, and hyperparameter optimization to build a predictive model for dispute outcomes.
  • You identified key factors influencing dispute predictions using interpretability tools.
  • You assessed the model’s predictive performance through metrics and visualizations, ensuring actionable insights for improving complaint resolution strategies.

By completing this tutorial, you should now be equipped to confidently set up and analyze NLP experiments in H2O Driverless AI. For further guidance or to strengthen your foundational skills, refer back to Tutorial 1A: Introduction to H2O Driverless AI.

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