# Linear Regression in Python

## Learn how to build and evaluate a Linear regression using Python’s Scikit-learn package.

# 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:

**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.

- Import all necessary library

2. Now we are going to read the data using pandas *read_csv** *function.(use **pandas.read_json** to read json data)

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.

Predicting the test data using the regression model .

The equation made here is a simple linear equation (Y = b0 + b1* X , b0 is intercept and b1 is the slope/coefficient of the equation.)

Accuracy of model is 99% .

U can find the code link here.

Thank you for the reading..

This is my first blog .I would love to have your feedbacks.