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model-interpretability

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Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

  • Updated Jun 9, 2021
  • Jupyter Notebook

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

  • Updated Aug 11, 2024
  • Jupyter Notebook

A major gas and electricity utility that supplies to SME. The power-liberalization of the energy market in Europe has led to significant customer churn.Building a churn model to understand whether price sensitivity is the largest driver of churn.Verifying the hypothesis of price sensitivity being to some extent correlated with churn.

  • Updated Apr 2, 2022
  • Jupyter Notebook

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