Sleep Disorder Detection with Bayesian Machine Learning

With Bayes_SleepDisorder.ipynb and sleep.csv dataset, the file can be run in .jupyter notebooks or Google Colab. The goals for this project are focusing on:

1. Describe the uncertainty in a sleep disorder diagnosis given lifestyle and sleep quality measurements

2. Evaluate the posterior probability distributions of the regression parameters for the predictor variables from above (lifestyle/sleep predictors)

3. Evaluate the model predictions and the uncertainties in these predictions.