Linear Regression
Linear Regression is the most basic algorithm in Machine Learning. It is a regression algorithm which means that it is useful when we are required to predict continuous values, that is, the output variable ‘y’ is continuous in nature. A few examples of the regression problem can be the following- 1.“What is the market value of the house?” 2.“Stock price prediction” 3.“Sales of a shop” 4.“Predicting height of a person” Terms to be used here: 1. Features -These are the independent variables in any dataset represented by x1 , x2 , x3, x4,... xn for ‘n’ features. 2. Target / Output Variable -This is the dependent variable whose value depends the independent variable by a relation (given below) and is represented by ‘y’. 3 .Function or Hypothesis of Linear Regression is represented by -y = m1.x1 + m2.x2 + m3.x3 + ... + mn.xn + b Note: Hypothesis is a function that tries to fit the data. 4. Intercept -Here b is the intercept of the line. We usually include this ‘b’ in the equatio...