Regression - The Process and Results

Regression Method

After using the linear model to generate housing price predictions, I plotted the predicted results and the associated residual on a residual plot, which showed non-linear patterns (i.e. the blue line does not match up with the red line)

Residual plot for values fitted with linear regression

After that, I decided to use GAM to model the housing price for more accurate predictions. In order to do that effectively, I used LASSO to pick out the top predictors that have the most impact on the housing price.

Picking the most optimal penalty for LASSO

Top 6 predictors using LASSO at the most optimal penalty

Finally, after using GAM, the residual plot is improved significantly since the GAM model captures the non-linearity in using the top 6 variables to predict house prices in Melbourne.

Using GAM to capture the non-linearity in housing price prediction

Residual plot for GAM-predicted results