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 the Apriori algorithm for product bundling

For now, we have focused on clients that are decreasing their purchases to create specific offers for them for products that they are not buying, but we can also improve the results for those that are already loyal customers. We can improve the number of products that they are buying by doing a market basket analysis and offering products that relate to their patterns of consumption. For this, we can use several algorithms.

One of the most popular methods for association rule learning is the Apriori algorithm. It recognizes the things in a data collection and expands them to ever-larger groupings of items. Apriori is employed in association rule mining in datasets to search for several often-occurring sets of things. It expands on the itemsets’ connections and linkages. This is the implementation of the “You may also like” suggestions that you frequently see on recommendation sites are the result of an algorithm...