## Python for Machine Learning | Visualize Iris Data with Seaborn and Matplotlib | Visualisation – P51

Python for Machine Learning | Visualize Iris Data with Seaborn and Matplotlib | Visualisation – P51

Visualize Iris Dataset with Seaborn and Matplotlib

Link for Github Repository – https://github.com/technologycult/PythonForMachineLearning/tree/master/Part51

Topics to be Covered –
1. Seaborn lmplot for plotting linear regression.
2. Generate a residual plot.
3. Generate a Scatter plot.
4. Plot a linear regression between the variables of iris dataset by specifing the hue.
5. Plot a linear regression between the variables of iris dataset grouped by row-wise.
6. Make a striplot of SL, SW and PL, PW grouped by Species.
7. Generate a swarmplot.
8. Generate a Violin plot
9. Generate a joint plot
10. Pairwise joint Distribution
11. Pairwise joint Distribution grouped by Species
12. Heatmap
13. Boxplot
14. kdeplot
15. Andrews Curve

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

1. Seaborn lmplot for plotting linear regression.

sns.lmplot(x=’SepalLength’,y=’SepalWidth’,data=iris)
sns.lmplot(x=’PetalLength’,y=’PetalWidth’,data=iris)

2. Generate a residual plot.

3. Generate a Scatter plot.

plt.scatter(iris[‘SepalLength’], iris[‘SepalWidth’], label=’iris’, color=’red’,marker = ‘o’)

sns.regplot(x = iris[‘SepalLength’], y =iris[‘SepalWidth’], data = ‘iris’, color=’red’, label = ‘Order 1’, order=1)
sns.regplot(x = iris[‘SepalLength’], y =iris[‘SepalWidth’], data = ‘iris’, color=’blue’, label = ‘Order 2’, order=2)
sns.regplot(x = iris[‘SepalLength’], y =iris[‘SepalWidth’], data = ‘iris’, color=’yellow’, label = ‘Order 3′, order=3)

4. Plot a linear regression between the variables of iris dataset by specifing the hue.
sns.lmplot(x=’SepalLength’,y=’SepalWidth’,data=iris,hue=’Species’, palette=’Set1′)

5. Plot a linear regression between the variables of iris dataset grouped by row-wise.
sns.lmplot(x=’SepalLength’,y=’SepalWidth’,data=iris,row=’Species’)

6. Make a striplot of SL, SW and PL, PW grouped by Species.
plt.subplot(2,2,1)
sns.stripplot(x=’Species’,y=’SepalLength’,data=iris)

plt.subplot(2,2,2)
sns.stripplot(x=’Species’,y=’SepalWidth’,data=iris, jitter = True, size=4)

7. Generate a swarmplot.
sns.swarmplot(x=’Species’, y = ‘SepalWidth’, data=iris)
sns.swarmplot(x=’Species’, y = ‘SepalLength’, data=iris)

8. Generate a Violin plot
sns.violinplot(x=’Species’, y = ‘SepalWidth’, data=iris)

9. Generate a joint plot
sns.jointplot(x=’SepalLength’, y = ‘SepalWidth’, data=iris)

10. Pairwise joint Distribution
sns.jointplot(x=’SepalLength’, y = ‘SepalWidth’, data=iris, kind=’resid’)

11. Pairwise joint Distribution grouped by Species

sns.pairplot(iris)

sns.residplot(x = iris[‘SepalLength’], y =iris[‘SepalWidth’], color=’red’)
sns.residplot(x = iris[‘PetalLength’], y =iris[‘PetalWidth’], color=’red’)

All Playlist of this youtube channel
====================================

1. Data Preprocessing in Machine Learning

2. Confusion Matrix in Machine Learning, ML, AI

3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz

4. Cross Validation, Sampling, train test split in Machine Learning

5. Drop and Delete Operations in Python Pandas

6. Matrices and Vectors with python

7. Detect Outliers in Machine Learning

8. TimeSeries preprocessing in Machine Learning

9. Handling Missing Values in Machine Learning

10. Dummy Encoding Encoding in Machine Learning

11. Data Visualisation with Python, Seaborn, Matplotlib

12. Feature Scaling in Machine Learning

13. Python 3 basics for Beginner

14. Statistics with Python

15. Sklearn Scikit Learn Machine Learning

16. Python Pandas Dataframe Operations

17. Linear Regression, Supervised Machine Learning

Source

### Comment List

• MachineLearning with Python
December 1, 2020

in any of your video, no use is mentioned

• MachineLearning with Python
December 1, 2020

Sir in Jupyter iam getting Module Error like " no module for pandas.tools" so andrews curve as well as radviz curve didnt executed

• MachineLearning with Python
December 1, 2020

Please put the source code for all lessons in guthub. This will help the learners