Search⌘ K
AI Features
Home
Courses
Introduction to Data Science with Python

Regression Plots

Explore how to use Seaborn's lmplot method for creating regression plots to visually analyze patterns in datasets. Understand how to customize plots with hues, markers, palettes, and grids, adjust figure sizes, and apply logistic regression for binary outcomes. This lesson helps you apply regression visualization techniques to extract meaningful insights from data using Python.

We'll cover the following...

Seaborn has many built-in capabilities for regression plots. The regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses. Let’s learn about the lmplot() method in this lesson.

First things first, we need to import seaborn and matplotlib.

C++
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
print(tips.head())

The lmplot() method

The lmplot() method allows us to display linear models with seaborn. The method plots data, as well as a regression model across a FacetGrid. This function actually combines regplot() and FacetGrid.

Understanding the difference between regplot() and lmplot() can be a bit tricky. In fact, they are closely related, since lmplot() uses regplot() internally and ...