Week 1: Simple Linear Regression

This assignment uses linear regression to predict house prices nbviewer

Week 2: Multiple Linear Regression

This builds upon the previous week using more variables as predictors of house prices nbviewer

Week 3: Polynomial Regression

Using polynomial regression to try and improve the fit of our model and visualise the effect of adding higher order polynomial terms on our line of best fit. nbviewer

Week 4 part 1: Ridge Regression (L2 regularisation)

We run ridge regression to try and avoid overfitting caused by adding polynomial terms to our regresssion. To find the optimal value of our L2 paramter we run Kfold cross validation. nbviewer

Week 4 part 2: Ridge Regression with Gradient Descent

In this final assignment we implement ridge regression using a gradient descent algorithm. nbviewer