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)
End User License Agreement

Using the Breast Cancer Wisconsin (Diagnostic) Data Set

Scikit‐learn ships with the Breast Cancer Wisconsin (Diagnostic) Data Set. It is a classic dataset that is often used to illustrate binary classifications. This dataset contains 30 features, and they are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The label of the dataset is a binary classification—M for malignant or B for benign. Interested readers can check out more information at

Examining the Relationship Between Features

You can load the Breast Cancer dataset by first importing the datasets module from sklearn. Then use the load_breast_cancer() function as follows:

from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer() 

Now that the Breast Cancer dataset has been loaded, it is useful to examine the relationships between some of its features.

Plotting the Features in 2D