The data will be loaded using Python Pandas, a data analysis module. The package NumPy is a fundamental Python scientific package that allows many high-performance operations on single- and multi-dimensional arrays. The first step is to load the dataset. Linear regression is a commonly used predictive analysis model. We create two arrays: X (size) and Y (price). This example uses the only the first feature of the diabetes dataset, in order to illustrate a two-dimensional plot of this regression technique. Import the following: import pandas as pd. This module highlights the use of Python linear regression, what linear regression is, the line of best fit, and the coefficient of x. It’s time to start implementing linear regression in Python. sklearn.linear_model.LinearRegression¶ class sklearn.linear_model.LinearRegression (*, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None) [source] ¶. Basically, all you should do is apply the proper packages and their functions and classes. Ordinary least squares Linear Regression. Python Packages for Linear Regression. Linear Regression Example¶. It will be loaded into a structure known as a Panda Data Frame, which allows for each manipulation of the rows and columns. I like to import all the necessary libraries on top of the notebook to keep everything organized. The advantage of working with Python is that we have access to many libraries that allow us to rapidly read data, plot the data, and perform a linear regression. In this blog post, I want to focus on the concept of linear regression and mainly on the implementation of it in Python. Linear regression is a statistical model that examines the linear relationship between two (Simple Linear Regression ) or more (Multiple Linear Regression) variables — a dependent variable and independent variable(s).