Visualize Regression Models with Seaborn


In this article, I will explain how to Visualize Regression Models with Seaborn. To begin with, let us first understand Regression Models.

Regression Models

In order to find the relationship between two variables, we create a regression model. Basically, regression analysis or regression modeling is a predictive modeling technique where we have an independent variable and a dependent variable. The regression analysis tells us that how the dependent variable takes its values according to the independent variable. When we plot the values that the two variables assume, we get a regression line. In other words, we get the best fit line that passes through the plotted data points of two variables such that the distance between the line and the points is minimized.

Seaborn Library of Python

In particular, the Seaborn library offers different plotting functions that work on data frames.

Python Program to Visualize Regression Models with Seaborn

The following python program demonstrates two regression plots. At first, we need to import the seaborn library. After that, we read the dataset file. Further, we remove the rows with missing values using the dropna() function. While the regplot() function plots the regression model. It takes the x, and y variables, and data frame as input. Also, order=2, indicates polynomial regression. Similarly, logistic = true represents logistic regression.

Furthermore, you can download the dataset file stroke_data.csv from here.

import seaborn as sb
from matplotlib import pyplot as plt
import pandas as pd



#Handling Missing values

sb.regplot(x="age", y="bmi", data=df2,
                 order=2, ci=None, scatter=None)
plt.title("Polynomial Regression")

sb.regplot(x="bmi", y="stroke", data=df2,
                 logistic=True, n_boot=500, y_jitter=.03)
plt.title("Logistic Regression")


The Output of the  Python Program to Visualize Regression Models with Seaborn
The Output of the Python Program to Visualize Regression Models with Seaborn

Plotting the Polynomial Regression between the bmi and age

In fact, the polynomial regression is a variation of the linear regression where a polynomial of nth degree depicts the relationship between the independent variable and the dependent variable rather than a straight line.

Plotting the Polynomial Regression
Plotting the Polynomial Regression

As can be seen in the above figure, BMI (Body Mass Index) increases with the age. However, after reaching its maximum value in the range [40-50], it starts decreasing again. Therefore, we can use a polynomial regression plot to represent this relationship.

Plotting the Logistic Regression between the stroke and BMI

When we have a dependent variable that takes discrete values, we can use logistic regression. The following figure shows an example of logistic regression. In fact, the variable bmi takes continuous values. While the variable stroke is discrete.

 Plotting the Logistic Regression
Plotting the Logistic Regression

Further Reading

Deep Learning Tutorial

Text Summarization Techniques

How to Implement Inheritance in Python

Find Prime Numbers in Given Range in Python

Running Instructions in an Interactive Interpreter in Python

Deep Learning Practice Exercise

Python Practice Exercise

Deep Learning Methods for Object Detection

Understanding YOLO Algorithm

What is Image Segmentation?

ImageNet and its Applications

Image Contrast Enhancement using Histogram Equalization

Transfer Learning and its Applications

Examples of OpenCV Library in Python

Examples of Tuples in Python

Python List Practice Exercise

Understanding Blockchain Concepts

Edge Detection Using OpenCV

Predicting with Time Series

Example of Multi-layer Perceptron Classifier in Python

Measuring Performance of Classification using Confusion Matrix

Artificial Neural Network (ANN) Model using Scikit-Learn

Popular Machine Learning Algorithms for Prediction

Long Short Term Memory – An Artificial Recurrent Neural Network Architecture

Python Project Ideas for Undergraduate Students

Creating Basic Charts using Plotly

Visualizing Regression Models with lmplot() and residplot() in Seaborn

Data Visualization with Pandas

A Brief Introduction of Pandas Library in Python

A Brief Tutorial on NumPy in Python


You may also like...