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

Scaling features to a range

When working with machine learning models, it is important to preprocess data so certain problems such as an explosion of gradients or lack of proper distribution representation can be solved.

To transform raw feature vectors into a representation that is better suited for the downstream estimators, the sklearn.preprocessing package offers a number of common utility functions and transformer classes.

Many machine learning estimators used in scikit-learn frequently require dataset standardization; if the individual features do not more or less resemble standard normally distributed data, they may behave poorly: Gaussian with a mean of 0 and a variation of 1.

In general, standardizing the dataset is advantageous for learning algorithms. Robust scalers or transformers are preferable if there are any outliers in the collection. On a dataset with marginal outliers, the actions of several scalers, transformers, and normalizers are highlighted in the analysis...