We are going to discuss :-
- Linear regression
- Polynomial regression
- Multivariate regression
It is very simple machine learning algorithm. In this we try to fit a straight line on our data sample.
y = m*x+c
This algorithm finds best fit values for m and c which are slope and intercept of the line.
It is very similar to linear regression. Here order of the equation is greater than 1.
For example :-
y = a*x^3 + b*x^2 + c*x + d
here this algorithm learns best values for a,b,c,d.
When we have more than one feature then we need multivariate regression.
For example :- price of a house might depend upon various parameters like number of rooms , number of floors, Area etc.
So price = a*number of rooms + b*number of floors + c*Area
Here algorithm learns best values for a,b,c. This set how important a feature is for prediction.