Introducing regression algorithms
Regression models are used where you are trying to predict a numeric outcome such as what price an item will sell for. The outcome variable is your target and your input variables are used to determine the relationship between the variables so that you can predict the unknown target on sets of data without the target variable.
You can have a single input variable, also known as simple linear regression. For example, years of experience and salary usually have a relationship.
Multiple linear regression is when you have multiple input variables. For example, predicting the selling price of homes in a particular zip code by using the relationship between the target (price) and various inputs such as square footage, number of bedrooms, pool, basement, lot size, and year built.
A good linear regression model has a small amount of vertical distance between the line and the data points. Refer to the following figure:
Figure...
