Linear regression is a common "baseline" method used for inital data exploration and analysis. It is a simple model that assumes a linear relationship between the input variables and the output variable. The model is defined by the equation:
y = b0 + b1x1 + b2x2 + ... + bn*xn
where y is the output variable, b0 is the bias term, and b1 to bn are the coefficients for the input variables x1 to xn.
Ensure the requirements are installed (see requirements.txt in the root project directory).
cd lin-reg-torch # Change to the directory containing the code from the root project directory
python lin-reg-torch.pySee the ./plots/loss_vs_epoch_manual_vs_torch.pdf file for a comparison of the loss vs epoch for the manual and torch implementations of linear regression.
By manual I mean that the model is implemented from scratch using numpy and torch for gradient computation. And by torch I mean that the model is implemented using the torch.nn.Linear module.