sklearn: Make your first linear regression model in Python [Video]

Written by Matt Dancho

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Scikit Learn is a powerful package for making machine learning models. In this Python Tip, we cover how to make your first Linear Regression Model that adds a trendline to a plot.

In this short tutorial, you’ll make a Linear Regression Trendline Plot with Sklearn.

This article is part of Python-Tips Weekly, a bi-weekly video tutorial that shows you step-by-step how to do common Python coding tasks.

Get the code

Here are the links to get set up. 👇

• Get the Code
• YouTube Tutorial

Sklearn Linear Regression Modeling Video Tutorial
For those that prefer Full YouTube Video Tutorials.

Learn how to use sklearn to make a linear regression model and plot with a trendline.

Before we get started, get the Python Cheat Sheet

I’ll use the Ultimate Python Cheat Sheet to access scikit learn documentation in this tutorial.

Ultimate Python Cheat Sheet:

First, Download the Ultimate Python Cheat Sheet. This gives you access to the entire Python Ecosystem at your fingertips via hyperlinked documenation and cheat sheets.

Click On Scikit-Learn

Navigate to the modeling section, and click on “Scikit Learn”.

Explore Scikit Learn

Now, you have access to the Scikit Learn Documentation at your fingertips.

Onto the tutorial.

Project: Making a Regression Trendline Plot

Let’s check out how to make a professional regression trendline plot with Scikit Learn.

Get the code.

Step 1: Load Libraries and Data

First, let’s load the libraries and data. From the libraries, we’ll import numpy and pandas along with LinearRegression and r2_score from sklearn to start out.

Get the code.

We’ll also load the mpg_df data set.

Get the code.

Step 2: Fit the Linear Regression Model

Goal: Understand the relationship between Fuel Economy (MPG) and Vehicle Weight.

Next, we can assess the relationship between vehicle fuel economy and vehicle weight using a Linear Regression Model. We fit the model first.

Code

We’ll use the LinearRegression() method from sklearn.linear_model to train a Linear Regression Model. This is the same process as in the Sklearn Documentation for OLS Regression.

Get the code.

Step 3: Making Predictions

We can use the trained (fitted) Linear Regression Model to make predictions. Simply call the predict() method on a Pandas Data Frame containing vehicle weights. A Numpy Array is returned with predictions for the vehicle fuel economy (MPG).

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Step 4: Visualize with Plotnine

The last step is to visualize the relationship between fuel economy and vehicle weight. We can use plotnine.

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The visualization that is return clearly shows an inverse trend between vehicle fuel economy and weight.

Summary

This was a short introduction to Scikit Learn, which is a foundational machine learning and modeling library in Python. We saw how we can use sklearn to make a linear regression model, and visualize the model prediction as a relationship with plotnine.

But, this was a simple problem and you’re eventually going to want to solve real-world problems that are much more complex:

• Most data science projects require much more data wrangling, visualization and reporting.

• Most data science teams use Pandas and Scikit Learn

• Many organizations are transitioning to automations (producing reports and data insights on-demand)

So, it makes sense to eventually learn Pandas and Scikit Learn to help with communication and working on R/Python teams.

If you’d like to learn data science for business with Pandas, Sktime, Plotnine, and more Python packages then read on. 👇

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