We are going to discuss :-

- Linear regression
- Polynomial regression
- Multivariate regression

**Linear 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.

**Polynomial**

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.

**Multivariate regression**

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.

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## Published by Karan Jakhar

A traveler in the field of Data Science.
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