About the project
What it is
I built this project to understand linear regression as a probability model rather than only a line-fitting method. It starts with a Gaussian likelihood and prior, then works through MAP estimation and the full posterior using the California Housing dataset.
The notebook studies how prior choices affect coefficients and how uncertainty in those coefficients becomes uncertainty in a prediction. Residual plots are used to see where the simple model still falls short.
