Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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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 

Figure 4.16 shows the scatter plot.

“Illustration depicting the plotting of a scatterplot that uses dots to represent the values for two different variables.”

Figure 4.16: Plotting a 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...