Book Image

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Book Image

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Table of Contents (12 chapters)
Data Science for Marketing Analytics
Preface

Performing and Interpreting Linear Regression


Linear regression is a type of regression model that uses linear relationships between predictors and the outcome to predict the outcome. Linear regression models can be thought of as a line running through the feature space that minimizes the distance between the line and the data points. This is best visualized when there is a single predictor (see Figure 5.14), where it is equivalent to drawing a line of best fit on a scatterplot between the two variables but can be generalized to many predictors:

Figure 5.16: A visualization of a linear regression line (red) fit to data (blue data points)

The line is generated by trying to find the line that best minimizes the error (difference) between the line and the data points. We'll learn more about types of errors in the next chapter, where we'll learn to use them to evaluate models, but it's important to note that they are also used in the process of fitting the model.

One of the big benefits of linear...