Complete Linear Regression for Machine learning:

Introduction to linear regression:

• Linear Regression is Machine learning algorithm based on supervised learning.
• In regression there are two types of variables i.e, Dependent and independent variables.
• The dependent variable is also known as target variable which we try to predict, and the independent variables, also known as explanatory variables.
• The independent variables are shown conventionally by x; and the dependent variable is notated by y.
• A regression model relates y, or the dependent variable, to a function of x, i.e., the independent variables. This model finds the linear relationship between independent and dependent variable.
• The key point in the regression is that our dependent value should be continuous, and cannot be a discrete value.
• However, the independent variable or variables can be measured on either a categorical or continuous measurement scale
• So, what we want to do here is to use historical data,using one or more of their features, and from that data, make a model.
• We use regression to build such a regression or estimation model.

Types of linear regression:

There are 2 types of linear regression models:

• Simple linear regression
• Multiple linear regression.

APPLICATIONS: