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)
Part 1: Data Analytics and Forecasting with Python
Part 2: Market and Customer Insights
Part 3: Operation and Pricing Optimization

Using related queries to get insights on new trends

If we want to find more information about the terms that are most associated with the search terms we are looking for, we can use the related queries to obtain queries that are similar to the ones we are searching for. This is useful because it provides not only contextual information but also information about trends that can be further analyzed.

In the next block of code, we will define a series of regions in which we want to look for the related queries for a given timeframe. In this case, we will be looking at the USA, Canada, New Zealand, and Australia. The results will be arranged into a single pandas DataFrame:

geo = ['US','CA','NZ','AU']
d_full = pd.DataFrame()
for g in geo:
  geo=g,timeframe='today 3-m')
  #get related queries
  related_queries = pytrend...