In Chapter 6, Intermediate Statistics and Graphs, we read about intermediate statistics with linear regression. I will continue this chapter from that point. Linear regression is already an algorithm you can use for predictions. You can make predictions with the directed, or the supervised, algorithms. Supervised algorithms have a target, or a dependent variable. They try to explain the values of that variable with the formula and the values of the independent variables. This explanation is stored in a model, which you use to predict the target variable value on a new dataset. The dependent variable supervises the development of the model. In a real project, you create many models, and then you deploy the one that suits your business needs the best. Therefore, you need to evaluate the models before the deployment.

In this chapter, I will explain the following: