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

OLS with Python and Statsmodels

OLS, a kind of linear least squares approach, is used in statistics to estimate unidentified parameters in a linear regression model. By minimizing the sum of squares of the differences between the observed values of the dependent variable and the values predicted by the linear function of the independent variable, OLS derives the parameters of a linear function from a set of explanatory variables in accordance with the least squares principle.

As a reminder, a linear regression model establishes the relationship between a dependent variable (y) and at least one independent variable (x), as follows:

In the OLS method, we have to choose the values of and , such that the total sum of squares of the difference between the calculated and observed values of is minimized.

OLS can be described in geometrical terms as the sum of all the squared distances between each point to the regression surface. This distance is measured...