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

Exploring variable relationships

Exploring the way in which variables move together can help us to determine the hidden patterns that govern the behaviors of our clients:

  1. Our first step here will be using the Seaborn method to plot some of the relationships, mostly between numerical continuous variables such as tenure, monthly charges, and total charges, using the churn as the hue parameter:
    g = sns.pairplot(data[['tenure','MonthlyCharges', 'TotalCharges','Churn']], hue= "Churn",palette= (["red","blue"]),height=6)

Figure 7.10: Continuous variable relationships

We can observe from the distributions that the customers who churn tend to have a low tenure number, generally having low amounts of monthly charges, as well as having much lower total charges on average.

  1. Now, we can finally transform and determine the object columns that we will convert into dummies:
    object_cols ...