In machine learning, linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. In this article, I will introduce you to linear regression with the Python programming language.

## Introduction to Linear Regression in Machine Learning

Linear Regression is a machine learning algorithm which uses a dependent variable to predict future outcomes based on one or more independent variables. It measures the association between two variables. Linear regression analysis is the most widely used of all machine learning algorithms.

**Also, Read – 100+ Machine Learning Projects Solved and Explained.**

Simply put, linear regression is a statistical test applied to a set of data to define and quantify the relationship between the variables considered. It is simple to use and is still considered among the most powerful algorithms.

The use of the linear regression algorithm is important for the following reasons:

- Description: It helps to analyze the strength of the association between the result (dependent variable) and the predictor variables.
- Adjustment: It adjusts the effect of covariates or confounders.
- Predictors: It helps to estimate the important risk factors that affect the dependent variable.
- The extent of the prediction: It helps analyze the magnitude of the change in the independent variable of a “unit” that would affect the dependent variable.
- Prediction: It helps quantify new cases.

## Linear Regression with Python

Now in this section, I will take you through how to implement Linear Regression with Python programming language. I will start this task by importing the necessary Python libraries:

Now, I will load the dataset:

diabetes = datasets.load_diabetes()

## Training Linear Regression with Python

To train the linear regression algorithm using the Python programming language, I will first split the dataset into 80% training and 20% test sets:

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=0)

Now let’s train the model:

Now let’s plot our trained model by using the matplotlib library in Python:

## Conclusion

The Linear Regression model is used to test the relationship between two variables in the form of an equation. You can implement this model without using any library like sklearn also which you can learn from **here**.

I hope you liked this article on Linear Regression with Python programming language. Feel free to ask your valuable questions in the comments section below.