We previously trained and evaluated a model for predicting the price of a pizza. While you are eager to demonstrate the pizza price predictor to your friends and coworkers, you are concerned by the model's imperfect R-squared score and the embarrassment its predictions could cause you. How can you improve the model?
Recalling your personal pizza-eating experience; you might have some intuitions about other attributes of a pizza that are related to its price. For instance, the price often depends on the number of toppings on the pizza. Fortunately, your pizza journal describes toppings in detail; let's add the number of toppings to our training data as a second explanatory variable. We cannot proceed with simple linear regression, but we can use a generalization of simple linear regression that can use multiple explanatory variables called multiple linear regression. Multiple linear regression is given by the following model:
