# What is regression in machine learning?

Regression is a subfield of supervised machine learning. It aims from model to predict the continuous target variable/ dependent variable (Y). Example : salary, price, temperature etc.

# What is linear regression ?

Linear regression is a type of supervised learning algorithm, commonly used for predictive analysis .It is widely used in biological, behavioral and social sciences to describe possible relationships between a dependent variable/ target variable(Y) and one or more independent variable/ feature variables(X) using a best fit straight line (also known as regression line).

# Types of linear regression

There are two types of linear regression:

1. Simple linear regression- It is the simplest case where we have only one dependent variable(Y) and one independent variable(X).
`Equation Y = b0 + b1* X`

2. Multi linear regression-It is a generalization of simple linear regression to the case of more than one independent variables(X).

`Equation Y = b0 + b1*X1 + b2*X2 + b3*X3...`

Without data you’re just another person with an opinion.

Now we will learn how to build simple linear regression model using data. You can download the data from here.

1. Import all necessary library

3. Now we are going to check null values from our data.

Their is a null value in y column(target variable) .So we are going to replace null values with the medium of y columns data. fillna function is used here to replace null value with median .

4. Now we are going to divide our train and test dataset in ratio of 70:30 , using sklearn library import train_test_split.

Here X is the independent variable/feature variable and Y is the dependent variable/target variable

5. Predict the values of test data.