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

Predicting sales with Prophet

Forecasting a time series can be a challenging task if there are many different methods you can use and many different hyperparameters for each method. The Prophet library is an open source library designed to make predictions for univariate time series data sets. It is easy to use and designed to automatically find a good set of hyperparameters for the model to make competent predictions for data with standard trends and seasonal structure. We will learn how to use the Facebook Prophet package to predict the weekly sales time series:

  1. First, we will import the library and create a dataset that contains all the features described as either continuous variables or one-hot representations:
    from fbprophet import Prophet
    data = train.drop(['Year','Month','Week'],axis=1).merge(features.drop(['IsHoliday','Week'],axis=1),on=['Store','Date'])
    data = pd.concat([data.drop(['Type&apos...