Book Image

Python Machine Learning

By : Sebastian Raschka
Book Image

Python Machine Learning

By: Sebastian Raschka

Overview of this book

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Table of Contents (21 chapters)
Python Machine Learning
About the Author
About the Reviewers

Exploring the Housing Dataset

Before we implement our first linear regression model, we will introduce a new dataset, the Housing Dataset, which contains information about houses in the suburbs of Boston collected by D. Harrison and D.L. Rubinfeld in 1978. The Housing Dataset has been made freely available and can be downloaded from the UCI machine learning repository at

The features of the 506 samples may be summarized as shown in the excerpt of the dataset description:

  • CRIM: This is the per capita crime rate by town

  • ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft.

  • INDUS: This is the proportion of non-retail business acres per town

  • CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)

  • NOX: This is the nitric oxides concentration (parts per 10 million)

  • RM: This is the average number of rooms per dwelling

  • AGE: This is the proportion of owner-occupied units...