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Linear Regression Machine Learning | Examples

Linear Regression-

 

In Machine Learning,

  • Linear Regression is a supervised machine learning algorithm.
  • It tries to find out the best linear relationship that describes the data you have.
  • It assumes that there exists a linear relationship between a dependent variable and independent variable(s).
  • The value of the dependent variable of a linear regression model is a continuous value i.e. real numbers.

 

Also Read- Machine Learning Algorithms

 

Representing Linear Regression Model-

 

Linear regression model represents the linear relationship between a dependent variable and independent variable(s) via a sloped straight line.

 

 

The sloped straight line representing the linear relationship that fits the given data best is called as a regression line.

It is also called as best fit line.

 

Types of Linear Regression-

 

Based on the number of independent variables, there are two types of linear regression-

 

 

  1. Simple Linear Regression
  2. Multiple Linear Regression

 

1. Simple Linear Regression-

 

In simple linear regression, the dependent variable depends only on a single independent variable.

 

For simple linear regression, the form of the model is-

Y = β0 + β1X

 

Here,

  • Y is a dependent variable.
  • X is an independent variable.
  • β0 and β1 are the regression coefficients.
  • β0 is the intercept or the bias that fixes the offset to a line.
  • β1 is the slope or weight that specifies the factor by which X has an impact on Y.

 

There are following 3 cases possible-

 

Case-01: β1 < 0

 

  • It indicates that variable X has negative impact on Y.
  • If X increases, Y will decrease and vice-versa.

 

 

Case-02: β1 = 0

 

  • It indicates that variable X has no impact on Y.
  • If X changes, there will be no change in Y.

 

 

Case-03: β1 > 0

 

  • It indicates that variable X has positive impact on Y.
  • If X increases, Y will increase and vice-versa.

 

 

2. Multiple Linear Regression-

 

In multiple linear regression, the dependent variable depends on more than one independent variables.

 

For multiple linear regression, the form of the model is-

Y = β0 + β1X1 + β2X2 + β3X3 + …… + βnXn

 

Here,

  • Y is a dependent variable.
  • X1, X2, …., Xn are independent variables.
  • β0, β1,…, βn are the regression coefficients.
  • βj (1<=j<=n) is the slope or weight that specifies the factor by which Xj has an impact on Y.

 

To gain better understanding about Linear Regression,

Watch this Video Lecture

 

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