Book Image

The Art of Data-Driven Business

By : Alan Bernardo Palacio
Book Image

The Art of Data-Driven Business

By: Alan Bernardo Palacio

Overview of this book

One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You’ll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you’ll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you’ll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you’ll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you’ve learned in the real world.
Table of Contents (17 chapters)
1
Part 1: Data Analytics and Forecasting with Python
4
Part 2: Market and Customer Insights
9
Part 3: Operation and Pricing Optimization

Feature engineering

To be able to properly analyze the data as well as to model the clusters, we will need to clean and structure the data—a step that is commonly referred to as feature engineering—as we need to restructure some of the variables according to our plan of analysis.

In this section, we will be performing the next steps to clean and structure some of the dataset features, with the goal of simplifying the existing variables and creating features that are easier to understand and describe the data properly:

  1. Create an Age variable for a customer by using the Year_Birth feature, indicating the birth year of the respective person.
  2. Create a Living_With feature to simplify the marital status, to describe the living situation of couples.
  3. Create a Children feature to indicate the total number of children in a household—that is, kids and teenagers.
  4. Aggregate spending by product type to better capture consumer behaviors.
  5. Indicate parenthood...