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The Art of Data-Driven Business

You're reading from   The Art of Data-Driven Business Transform your organization into a data-driven one with the power of Python machine learning

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804611036
Length 314 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Analytics and Forecasting with Python
2. Chapter 1: Analyzing and Visualizing Data with Python FREE CHAPTER 3. Chapter 2: Using Machine Learning in Business Operations 4. Part 2: Market and Customer Insights
5. Chapter 3: Finding Business Opportunities with Market Insights 6. Chapter 4: Understanding Customer Preferences with Conjoint Analysis 7. Chapter 5: Selecting the Optimal Price with Price Demand Elasticity 8. Chapter 6: Product Recommendation 9. Part 3: Operation and Pricing Optimization
10. Chapter 7: Predicting Customer Churn 11. Chapter 8: Grouping Users with Customer Segmentation 12. Chapter 9: Using Historical Markdown Data to Predict Sales 13. Chapter 10: Web Analytics Optimization 14. Chapter 11: Creating a Data-Driven Culture in Business 15. Index 16. Other Books You May Enjoy

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:
  pytrend.build_payload(kw_list=['bitcoin','stocks'],
  geo=g,timeframe='today 3-m')
  #get related queries
  related_queries = pytrend...
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