• Regression is a supervised learning technique that supports finding the correlation among variables

• A regression problem is when the output variable is a real or continuous value.

• Regression shows a line or curve that passes through all the data points on a target-predictor graph in such a way that the vertical distance between the data points and the regression line is minimum.

  Types of Regression Models

  1. Linear Regression

  2. Polynomial Regression

  3. Logistics Regression

     Linear Regression

     • Linear regression is a quiet and simple statistical regression method used for predictive analysis and shows the relationship between the continuous variables

     • Linear regression shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis), consequently called linear regression*.*

     • If there is a single input variable (x), such linear regression is called**simple linear regression*.*

     • f there is more than one input variable, such linear regression is called**multiple linear regression.**

|y = mx + b|

• **y= Dependent Variable.

  x= Independent Variable.

  a0/b= intercept of the line.

  a1 /m= Linear regression coefficient.**

• A regression line can be a Positive Linear Relationship or a Negative Linear Relationship.

  Positive Linear Relationship

  If the dependent variable expands on the Y-axis and the independent variable progress on X-axis, then such a relationship is termed a Positive linear relationship.

• Negative Linear Relationship

  If the dependent variable decreases on the Y-axis and the independent variable increases on the X-axis, such a relationship is called a negative linear relationship.