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You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

Table of Contents (16) Chapters Close

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index
16. End User License Agreement

Plotting Scatter Plots

A scatter plot is a two‐dimensional chart that uses dots (or other shapes) to represent the values for two different variables. Scatter plots are often used to show how much the value of one variable is affected by another.

The following code snippet shows a scatter plot with the x‐axis containing a list of numbers from 1 to 4, and the y‐axis showing the cube of the x‐axis values:

````%matplotlib inline`
`import matplotlib.pyplot as plt`
` `
`plt.plot([1,2,3,4],        # x-axis`
`         [1,8,27,64],      # y-axis`
`         'bo')             # blue circle marker`
`plt.axis([0, 4.5, 0, 70])  # xmin, xmax, ymin, ymax`
`plt.show()` ```

Figure 4.16 shows the scatter plot.

Combining Plots

You can combine multiple scatter plots into one chart as follows:

````%matplotlib inline`
`import matplotlib.pyplot as plt`
` `
`import numpy as np`
` `
`a = np.arange(1,4.5,0.1)   # 1.0, 1.1, 1.2, 1.3…4.4`
`plt.plot(a, a**2, 'y...````
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