Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

Classification Models

The Boston Housing dataset was great for regression because the target column took on continuous values without limit. There are many cases when the target column takes on one or two values, such as TRUE or FALSE, or possibly a grouping of three or more values, such as RED, BLUE, or GREEN. When the target column may be split into distinct categories, the group of machine learning models that you should try are referred to as classification.

To make things interesting, let's load a new dataset used to detect pulsar stars in outer space. Go to and click on Data Folder. Then, click on

Figure 11.11: Dataset directory on the UCI website

The dataset consists of 17,898 potential pulsar stars in space. But what are these pulsars? Pulsar stars rotate very quickly, so they have periodic light patterns. Radio frequency interference and noise, however, are attributes that make pulsars very hard to detect...