How to run Linear regression in Python scikit-Learn

Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them.

You know that linear regression is a popular technique and you might as well seen the mathematical equation of linear regression. But do you know how to implement a linear regression in Python?? If so don’t read this post because this post is all about implementing linear regression in Python. There are several ways in which you can do that, you can do linear regression using numpy, scipy, stats model and sckit learn. But in this post I am going to use scikit learn to perform linear regression.

When implementing linear regression of some dependent variable 𝑦 on the set of independent variables 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of predictors, you assume a linear relationship between 𝑦 and 𝐱: 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀. This equation is the regression equation.

Scikit-learn is a powerful Python module for machine learning. It contains function for regression, classification, clustering, model selection and dimensionality reduction. Today, I will explore the sklearn.linear_model module which contains “methods intended for regression in which the target value is expected to be a linear combination of the input variables”.