Tabular Transformers for Clinical Biostatistics Data This notebook demonstrates how to apply Tabular Transformers to a clinical biostatistics dataset with a mix of numerical and categorical featur...

This notebook presents a framework for anomaly detection using autoencoders implemented in PyTorch. The approach leverages a neural network autoencoder for both dimensionality reduction and reconst...

Research Question: Does quitting smoking cause weight gain? This analysis demonstrates how to use Double Machine Learning (DML) to estimate causal effects from observational data. We analyze data ...

🔍 Interactive Visualizations Explore the data behind the collaboration paradox through these interactive infographics: 📊 View Full Infographic: The Collaboration Paradox 📈 Interactive Analysis: Th...

Unveiling the Unseen: A Guide to Array Variate Normal Distribution and Recovering Missing Data Ever stared at a dataset with gaping holes and wondered how to make sense of it? Data, especially in ...

TabularGAN for Anomaly Detection in Clinical Data This notebook demonstrates an application of Generative Adversarial Networks (GANs) for anomaly detection in structured clinical data using: A...