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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

Installing Pytrends and ranking markets

As a first step, we need to install the package that we will use to analyze the web search data. We will install the Pytrends package, which is a wrapper around the Google Trends API. To do this, open a new Jupyter notebook running Python 3.7, and in a new cell run the following command to install the package:

pip install pytrends

After the package has been installed, we can start the analysis. We can run several types of queries to the API, which are as follows:

  • Interest over time
  • Historical hourly interest
  • Interest by region
  • Related topics
  • Related queries
  • Trending searches
  • Real-time search trends
  • Top charts
  • Suggestions

In this case, we want to obtain information about where the interest per region in a given set of terms is. These are steps that we will follow:

  1. Import the pandas package for storing the results and plotting the data.
  2. Initialize the Pytrends API and we will pass...
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