Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Exploring feature engineering

Now that we made a system to fairly compare models with no fear of overfitting, let's think about how we can improve our model. One way would be to create new features that might add more context. One way to go about this is to create features of our own, for example, calculate a proportion of armies on different sides or the absolute difference in the number of soldiers—we can't say in advance which would work better. Let's try it out with the help of the following code:

  1. First, we'll create a ratio of soldiers on either side:
data['infantry_ratio'] = data['allies_infantry'] / data['axis_infantry']
cols.append('infantry_ratio')
  1. Now, we won't do that for tanks, planes, and so on, as the numbers here are very small and we'll have to deal with division by zero. Instead, we...