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

Building machine learning models

One of the most simple machine learning models we can construct to make a forecast of future behaviors is linear regression, which reduces the residual sum of squares between the targets observed in the dataset and the targets anticipated by the linear approximation, fitting a linear model using coefficients.

This is simply ordinary least squares or non-negative least squares wrapped in a predictor object from the implementation perspective.

We can implement this really simply by using the LinearRegression class in Sklearn:

from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_diabetes
data_reg = load_diabetes()
x,y = data_reg['data'],data_reg['target']
reg = LinearRegression().fit(x, y)
reg.score(x, y)

Figure 2.24: Model regression score

The preceding code will fit a linear regression model to our data and print the score of our data.

We can also print the coefficients...