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 customer data

Our goal is to create a model to estimate the likelihood of abandonment using data pertaining to Telecom customers. This is to answer the question of how likely it is that a consumer will discontinue utilizing the service.

Initially, the data is subjected to exploratory analysis. Knowing the data types of each column is the first step in the process, after which any necessary adjustments to the variables are made.

To explore the data, we will plot the relationships between the churn variable and the other important factors that make up the dataset. Prior to suggesting a model, this work is carried out to get a preliminary understanding of the underlying relationships between the variables.

A thorough approach is taken while performing descriptive statistics, which focus on client differences based on one or more attributes. The primary variable of interest, churn, is now the focus, and a new set of interesting graphs is produced for this reason.