Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

Featuretools on a New Dataset

In this chapter, we have learned about Featuretools and how to build automated features using it. In the next activity, we will apply what we have learned to a new dataset. This dataset is a modified version of the adult dataset from the UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, which can be found at https://packt.live/2Qr3ih6, in the adult.data file. This dataset has various attributes of a working adult, such as age, occupation, education, and native. The task is to predict whether a particular adult will earn more than 50,000 in their yearly salary or not.

The details about the various attributes are available at the preceding link in the adult.names file. This dataset has a mix of both categorical and numerical data and is a good dataset to try out what you have learned about Featuretools.

Activity 17.01: Building a Classification Model with Features that have been Generated...