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)
1
Part 1: Data Analytics and Forecasting with Python
4
Part 2: Market and Customer Insights
9
Part 3: Operation and Pricing Optimization

Predicting new feature combinations

Now that we have properly trained our predictor, we can use it besides capturing information about the product features’ importance to also provide us with information about how new product features will perform:

  1. After going through the EDA, we will develop some predictive models and compare them. We will use the DataFrame where we had created dummy variables, scaling all the variables to a range of 0 to 1:
    from sklearn.preprocessing import StandardScaler
    X = conjoint_dat_dum
    y = conjoint_dat['ranking'].astype(int)
    # The target variable will be normalized from a ranking to a 1 to 10 score
    y = y.apply(lambda x: int(x/len(y)*10))
    features = X.columns.values
    scaler = StandardScaler()
    scaler.fit(X)
    X = pd.DataFrame(scaler.transform(X))
    X.columns = features

This results in the following output:

Figure 4.14: Scaled variables

One of the most popular ML models is Logistic Regression, which is an algorithm that...